diff --git a/.github/workflows/ci-tensorflow-v1.yml b/.github/workflows/ci-tensorflow-v1.yml
index a66a082b14..ad287653a7 100644
--- a/.github/workflows/ci-tensorflow-v1.yml
+++ b/.github/workflows/ci-tensorflow-v1.yml
@@ -48,7 +48,7 @@ jobs:
sudo apt-get update
sudo apt-get -y -q install ffmpeg libavcodec-extra
python -m pip install --upgrade pip setuptools wheel
- pip install -q -r <(sed '/^pandas/d;/^scipy/d;/^matplotlib/d;/^xgboost/d;/^tensorflow/d;/^keras/d;/^jax/d;/^torch/d;/^Pillow/d;/^h5py/d;/^kornia/d;/^scikit-learn/d;/^pytest-mock/d;/^GPy/d;/^lief/d;/^statsmodels/d;/^ultralytics/d;/^ipython/d;/^numba/d;/^pytest/d;/^pylint/d;/^mypy/d;/^pycodestyle/d;/^black/d;/^types-PyYAML/d;/^types-setuptools/d' requirements_test.txt)
+ pip install -q -r <(sed '/^pandas/d;/^scipy/d;/^matplotlib/d;/^xgboost/d;/^tensorflow/d;/^keras/d;/^jax/d;/^torch/d;/^Pillow/d;/^h5py/d;/^kornia/d;/^scikit-learn/d;/^pytest-mock/d;/^GPy/d;/^lief/d;/^statsmodels/d;/^ultralytics/d;/^ipython/d;/^numba/d;/^pytest/d;/^pylint/d;/^mypy/d;/^pycodestyle/d;/^black/d;/^types-PyYAML/d;/^types-setuptools/d;/^requests/d' requirements_test.txt)
pip install pandas==1.3.5
pip install scipy==1.7.2
pip install matplotlib==3.5.3
@@ -71,6 +71,7 @@ jobs:
pip install numba==0.56.4
pip install pytest==7.4.4
pip install pytest-cov
+ pip install requests==2.31.0
pip list
- name: Run Tests
run: ./run_tests.sh ${{ matrix.framework }}
diff --git a/.github/workflows/dockerhub.yml b/.github/workflows/dockerhub.yml
index bcd676b6b0..91337e7c18 100644
--- a/.github/workflows/dockerhub.yml
+++ b/.github/workflows/dockerhub.yml
@@ -30,7 +30,7 @@ jobs:
- name: Extract metadata (tags, labels) for Docker
id: meta
- uses: docker/metadata-action@8e5442c4ef9f78752691e2d8f8d19755c6f78e81
+ uses: docker/metadata-action@369eb591f429131d6889c46b94e711f089e6ca96
with:
images: adversarialrobustnesstoolbox/releases
tags: |
@@ -38,7 +38,7 @@ jobs:
type=semver,pattern={{version}}
- name: Build and push Docker image
- uses: docker/build-push-action@4f58ea79222b3b9dc2c8bbdd6debcef730109a75
+ uses: docker/build-push-action@48aba3b46d1b1fec4febb7c5d0c644b249a11355
with:
context: .
push: true
diff --git a/art/attacks/evasion/__init__.py b/art/attacks/evasion/__init__.py
index dbee974ab0..d39c645285 100644
--- a/art/attacks/evasion/__init__.py
+++ b/art/attacks/evasion/__init__.py
@@ -14,6 +14,7 @@
from art.attacks.evasion.auto_attack import AutoAttack
from art.attacks.evasion.auto_projected_gradient_descent import AutoProjectedGradientDescent
from art.attacks.evasion.auto_conjugate_gradient import AutoConjugateGradient
+from art.attacks.evasion.rescaling_auto_conjugate_gradient import RescalingAutoConjugateGradient
if importlib.util.find_spec("numba") is not None:
from art.attacks.evasion.brendel_bethge import BrendelBethgeAttack
@@ -62,6 +63,7 @@
from art.attacks.evasion.shapeshifter import ShapeShifter
from art.attacks.evasion.simba import SimBA
from art.attacks.evasion.spatial_transformation import SpatialTransformation
+from art.attacks.evasion.steal_now_attack_later.steal_now_attack_later import SNAL
from art.attacks.evasion.square_attack import SquareAttack
from art.attacks.evasion.pixel_threshold import ThresholdAttack
from art.attacks.evasion.universal_perturbation import UniversalPerturbation
diff --git a/art/attacks/evasion/auto_attack.py b/art/attacks/evasion/auto_attack.py
index 01a4046ec7..a148e43b20 100644
--- a/art/attacks/evasion/auto_attack.py
+++ b/art/attacks/evasion/auto_attack.py
@@ -78,7 +78,7 @@ def __init__(
batch_size: int = 32,
estimator_orig: "CLASSIFIER_TYPE" | None = None,
targeted: bool = False,
- parallel: bool = False,
+ parallel_pool_size: int = 0,
):
"""
Create a :class:`.AutoAttack` instance.
@@ -93,7 +93,8 @@ def __init__(
:param estimator_orig: Original estimator to be attacked by adversarial examples.
:param targeted: If False run only untargeted attacks, if True also run targeted attacks against each possible
target.
- :param parallel: If True run attacks in parallel.
+ :param parallel_pool_size: Number of parallel threads / pool size in multiprocessing. If parallel_pool_size=0
+ computation runs without multiprocessing.
"""
super().__init__(estimator=estimator)
@@ -151,7 +152,7 @@ def __init__(
self.estimator_orig = estimator
self._targeted = targeted
- self.parallel = parallel
+ self.parallel_pool_size = parallel_pool_size
self.best_attacks: np.ndarray = np.array([])
self._check_params()
@@ -199,7 +200,7 @@ def generate(self, x: np.ndarray, y: np.ndarray | None = None, **kwargs) -> np.n
if attack.targeted:
attack.set_params(targeted=False)
- if self.parallel:
+ if self.parallel_pool_size > 0:
args.append(
(
deepcopy(x_adv),
@@ -253,7 +254,7 @@ def generate(self, x: np.ndarray, y: np.ndarray | None = None, **kwargs) -> np.n
targeted_labels[:, i], nb_classes=self.estimator.nb_classes
)
- if self.parallel:
+ if self.parallel_pool_size > 0:
args.append(
(
deepcopy(x_adv),
@@ -287,8 +288,8 @@ def generate(self, x: np.ndarray, y: np.ndarray | None = None, **kwargs) -> np.n
except ValueError as error:
logger.warning("Error completing attack: %s}", str(error))
- if self.parallel:
- with multiprocess.get_context("spawn").Pool() as pool:
+ if self.parallel_pool_size > 0:
+ with multiprocess.get_context("spawn").Pool(processes=self.parallel_pool_size) as pool:
# Results come back in the order that they were issued
results = pool.starmap(run_attack, args)
perturbations = []
@@ -320,7 +321,7 @@ def __repr__(self) -> str:
This method returns a summary of the best performing (lowest perturbation in the parallel case) attacks
per image passed to the AutoAttack class.
"""
- if self.parallel:
+ if self.parallel_pool_size > 0:
best_attack_meta = "\n".join(
[
f"image {i+1}: {str(self.args[idx][3])}" if idx != 0 else f"image {i+1}: n/a"
@@ -328,7 +329,8 @@ def __repr__(self) -> str:
]
)
auto_attack_meta = (
- f"AutoAttack(targeted={self.targeted}, parallel={self.parallel}, num_attacks={len(self.args)})"
+ f"AutoAttack(targeted={self.targeted}, parallel_pool_size={self.parallel_pool_size}, "
+ + "num_attacks={len(self.args)})"
)
return f"{auto_attack_meta}\nBestAttacks:\n{best_attack_meta}"
@@ -339,7 +341,8 @@ def __repr__(self) -> str:
]
)
auto_attack_meta = (
- f"AutoAttack(targeted={self.targeted}, parallel={self.parallel}, num_attacks={len(self.attacks)})"
+ f"AutoAttack(targeted={self.targeted}, parallel_pool_size={self.parallel_pool_size}, "
+ + "num_attacks={len(self.attacks)})"
)
return f"{auto_attack_meta}\nBestAttacks:\n{best_attack_meta}"
diff --git a/art/attacks/evasion/rescaling_auto_conjugate_gradient.py b/art/attacks/evasion/rescaling_auto_conjugate_gradient.py
new file mode 100644
index 0000000000..2b2e53a595
--- /dev/null
+++ b/art/attacks/evasion/rescaling_auto_conjugate_gradient.py
@@ -0,0 +1,664 @@
+# MIT License
+
+# Copyright (c) 2024 Keiichiro Yamamura
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2024
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the 'Rescaling-ACG' attack.
+
+| Paper link: https://arxiv.org/abs/2408.03972
+"""
+import abc
+import logging
+import math
+from typing import Optional, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import check_and_transform_label_format, projection, random_sphere, is_probability, get_labels_np_array
+
+if TYPE_CHECKING:
+ from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class RescalingAutoConjugateGradient(EvasionAttack):
+ """
+ Implementation of the 'Rescaling-ACG' attack.
+ The original implementation is https://github.com/yamamura-k/ReACG.
+
+ | Paper link:
+ """
+
+ attack_params = EvasionAttack.attack_params + [
+ "norm",
+ "eps",
+ "eps_step",
+ "max_iter",
+ "targeted",
+ "nb_random_init",
+ "batch_size",
+ "loss_type",
+ "verbose",
+ ]
+ _estimator_requirements = (BaseEstimator, LossGradientsMixin, ClassifierMixin)
+ _predefined_losses = [None, "cross_entropy", "difference_logits_ratio"]
+
+ def __init__(
+ self,
+ estimator: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+ norm: Union[int, float, str] = np.inf,
+ eps: float = 0.3,
+ eps_step: float = 0.1,
+ max_iter: int = 100,
+ targeted: bool = False,
+ nb_random_init: int = 5,
+ batch_size: int = 32,
+ loss_type: Optional[str] = None,
+ verbose: bool = True,
+ ):
+ """
+ Create a :class:`.RescalingAutoConjugateGradient` instance.
+
+ :param estimator: An trained estimator.
+ :param norm: The norm of the adversarial perturbation. Possible values: "inf", np.inf, 1 or 2.
+ :param eps: Maximum perturbation that the attacker can introduce.
+ :param eps_step: Attack step size (input variation) at each iteration.
+ :param max_iter: The maximum number of iterations.
+ :param targeted: Indicates whether the attack is targeted (True) or untargeted (False).
+ :param nb_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0
+ starting at the original input.
+ :param batch_size: Size of the batch on which adversarial samples are generated.
+ :param loss_type: Defines the loss to attack. Available options: None (Use loss defined by estimator),
+ "cross_entropy", or "difference_logits_ratio"
+ :param verbose: Show progress bars.
+ """
+ from art.estimators.classification import TensorFlowClassifier, TensorFlowV2Classifier, PyTorchClassifier
+
+ if isinstance(estimator, TensorFlowClassifier):
+ raise ValueError("This attack does not support TensorFlow v1.")
+
+ if loss_type not in self._predefined_losses:
+ raise ValueError(
+ f"The argument loss_type has an invalid value. The following options for `loss_type` are currently "
+ f"supported: {self._predefined_losses}"
+ )
+
+ if loss_type is None:
+ if hasattr(estimator, "predict") and is_probability(
+ estimator.predict(x=np.ones(shape=(1, *estimator.input_shape), dtype=np.float32))
+ ):
+ raise ValueError( # pragma: no cover
+ "AutoProjectedGradientDescent is expecting logits as estimator output, the provided "
+ "estimator seems to predict probabilities."
+ )
+
+ estimator_reacg = estimator
+ else:
+ if isinstance(estimator, TensorFlowV2Classifier):
+ import tensorflow as tf
+
+ class TensorFlowV2Loss:
+ """abstract class of loss function of tensorflow v2"""
+
+ @abc.abstractmethod
+ def __call__(self, y_true: tf.Tensor, y_pred: tf.Tensor, *args, **kwargs) -> tf.Tensor:
+ raise NotImplementedError
+
+ if loss_type == "cross_entropy":
+
+ class CrossEntropyLossV2(TensorFlowV2Loss):
+ """Class defining cross entropy loss with reduction options."""
+
+ def __init__(self, from_logits, reduction="sum"):
+ self.ce_loss = tf.keras.losses.CategoricalCrossentropy(
+ from_logits=from_logits,
+ reduction=tf.keras.losses.Reduction.NONE,
+ )
+ self.reduction = reduction
+
+ def __call__(self, y_true: tf.Tensor, y_pred: tf.Tensor, *args, **kwargs) -> tf.Tensor:
+ if self.reduction == "mean":
+ return tf.reduce_mean(self.ce_loss(y_true, y_pred))
+ if self.reduction == "sum":
+ return tf.reduce_sum(self.ce_loss(y_true, y_pred))
+ if self.reduction == "none":
+ return self.ce_loss(y_true, y_pred)
+ raise NotImplementedError()
+
+ if is_probability(estimator.predict(x=np.ones(shape=(1, *estimator.input_shape)))):
+ _loss_object_tf: TensorFlowV2Loss = CrossEntropyLossV2(from_logits=False)
+ else:
+ _loss_object_tf = CrossEntropyLossV2(from_logits=True)
+ elif loss_type == "difference_logits_ratio":
+ if is_probability(estimator.predict(x=np.ones(shape=(1, *estimator.input_shape)))):
+ raise ValueError( # pragma: no cover
+ "The provided estimator seems to predict probabilities. "
+ "If loss_type='difference_logits_ratio' the estimator has to to predict logits."
+ )
+
+ class DifferenceLogitsRatioTensorFlowV2(TensorFlowV2Loss):
+ """
+ Callable class for Difference Logits Ratio loss in TensorFlow v2.
+ """
+
+ def __init__(self):
+ self.reduction = "sum"
+
+ def __call__(self, y_true: tf.Tensor, y_pred: tf.Tensor, *args, **kwargs) -> tf.Tensor:
+ i_y_true = tf.cast(tf.math.argmax(tf.cast(y_true, tf.int32), axis=1), tf.int32)
+ i_y_pred_arg = tf.argsort(y_pred, axis=1)
+ i_z_i_list = []
+
+ for i in range(y_true.shape[0]):
+ if i_y_pred_arg[i, -1] != i_y_true[i]:
+ i_z_i_list.append(i_y_pred_arg[i, -1])
+ else:
+ i_z_i_list.append(i_y_pred_arg[i, -2])
+
+ i_z_i = tf.stack(i_z_i_list)
+
+ z_1 = tf.gather(y_pred, i_y_pred_arg[:, -1], axis=1, batch_dims=0)
+ z_3 = tf.gather(y_pred, i_y_pred_arg[:, -3], axis=1, batch_dims=0)
+ z_i = tf.gather(y_pred, i_z_i, axis=1, batch_dims=0)
+ z_y = tf.gather(y_pred, i_y_true, axis=1, batch_dims=0)
+
+ z_1 = tf.linalg.diag_part(z_1)
+ z_3 = tf.linalg.diag_part(z_3)
+ z_i = tf.linalg.diag_part(z_i)
+ z_y = tf.linalg.diag_part(z_y)
+
+ dlr = -(z_y - z_i) / (z_1 - z_3)
+ if self.reduction == "mean":
+ return tf.reduce_mean(dlr)
+ if self.reduction == "sum":
+ return tf.reduce_sum(dlr)
+ if self.reduction == "none":
+ return dlr
+ raise NotImplementedError()
+
+ _loss_object_tf = DifferenceLogitsRatioTensorFlowV2()
+ else:
+ raise NotImplementedError()
+
+ estimator_reacg = TensorFlowV2Classifier(
+ model=estimator.model,
+ nb_classes=estimator.nb_classes,
+ input_shape=estimator.input_shape,
+ loss_object=_loss_object_tf,
+ optimizer=estimator.optimizer,
+ train_step=estimator.train_step,
+ channels_first=estimator.channels_first,
+ clip_values=estimator.clip_values,
+ preprocessing_defences=estimator.preprocessing_defences,
+ postprocessing_defences=estimator.postprocessing_defences,
+ preprocessing=estimator.preprocessing,
+ )
+ elif isinstance(estimator, PyTorchClassifier):
+ import torch
+
+ if loss_type == "cross_entropy":
+ if is_probability(
+ estimator.predict(x=np.ones(shape=(1, *estimator.input_shape), dtype=np.float32))
+ ):
+ raise ValueError( # pragma: no cover
+ "The provided estimator seems to predict probabilities. If loss_type='cross_entropy' "
+ "the estimator has to to predict logits."
+ )
+
+ class CrossEntropyLossTorch(torch.nn.modules.loss._Loss): # pylint: disable=W0212
+ """Class defining cross entropy loss with reduction options."""
+
+ def __init__(self, reduction="sum"):
+ super().__init__()
+ self.ce_loss = torch.nn.CrossEntropyLoss(reduction="none")
+ self.reduction = reduction
+
+ def __call__(self, y_true: torch.Tensor, y_pred: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+ if self.reduction == "mean":
+ return self.ce_loss(y_true, y_pred).mean()
+ if self.reduction == "sum":
+ return self.ce_loss(y_true, y_pred).sum()
+ if self.reduction == "none":
+ return self.ce_loss(y_true, y_pred)
+ raise NotImplementedError()
+
+ def forward(
+ self, input: torch.Tensor, target: torch.Tensor # pylint: disable=W0622
+ ) -> torch.Tensor:
+ """
+ Forward method.
+ :param input: Predicted labels of shape (nb_samples, nb_classes).
+ :param target: Target labels of shape (nb_samples, nb_classes).
+ :return: Difference Logits Ratio Loss.
+ """
+ return self.__call__(y_true=target, y_pred=input)
+
+ _loss_object_pt: torch.nn.modules.loss._Loss = CrossEntropyLossTorch(reduction="mean")
+
+ elif loss_type == "difference_logits_ratio":
+ if is_probability(
+ estimator.predict(x=np.ones(shape=(1, *estimator.input_shape), dtype=ART_NUMPY_DTYPE))
+ ):
+ raise ValueError( # pragma: no cover
+ "The provided estimator seems to predict probabilities. "
+ "If loss_type='difference_logits_ratio' the estimator has to to predict logits."
+ )
+
+ class DifferenceLogitsRatioPyTorch(torch.nn.modules.loss._Loss): # pylint: disable=W0212
+ """
+ Callable class for Difference Logits Ratio loss in PyTorch.
+ """
+
+ def __init__(self):
+ super().__init__()
+ self.reduction = "sum"
+
+ def __call__(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
+ if isinstance(y_true, np.ndarray):
+ y_true = torch.from_numpy(y_true)
+ if isinstance(y_pred, np.ndarray):
+ y_pred = torch.from_numpy(y_pred)
+
+ y_true = y_true.float()
+
+ i_y_true = torch.argmax(y_true, dim=1)
+ i_y_pred_arg = torch.argsort(y_pred, dim=1)
+ i_z_i_list = []
+
+ for i in range(y_true.shape[0]):
+ if i_y_pred_arg[i, -1] != i_y_true[i]:
+ i_z_i_list.append(i_y_pred_arg[i, -1])
+ else:
+ i_z_i_list.append(i_y_pred_arg[i, -2])
+
+ i_z_i = torch.stack(i_z_i_list)
+
+ z_1 = y_pred[:, i_y_pred_arg[:, -1]]
+ z_3 = y_pred[:, i_y_pred_arg[:, -3]]
+ z_i = y_pred[:, i_z_i]
+ z_y = y_pred[:, i_y_true]
+
+ z_1 = torch.diagonal(z_1)
+ z_3 = torch.diagonal(z_3)
+ z_i = torch.diagonal(z_i)
+ z_y = torch.diagonal(z_y)
+
+ dlr = (-(z_y - z_i) / (z_1 - z_3)).float()
+ if self.reduction == "mean":
+ return dlr.mean()
+ if self.reduction == "sum":
+ return dlr.sum()
+ if self.reduction == "none":
+ return dlr
+ raise NotImplementedError()
+
+ def forward(
+ self, input: torch.Tensor, target: torch.Tensor # pylint: disable=W0622
+ ) -> torch.Tensor:
+ """
+ Forward method.
+ :param input: Predicted labels of shape (nb_samples, nb_classes).
+ :param target: Target labels of shape (nb_samples, nb_classes).
+ :return: Difference Logits Ratio Loss.
+ """
+ return self.__call__(y_true=target, y_pred=input)
+
+ _loss_object_pt = DifferenceLogitsRatioPyTorch()
+ else:
+ raise NotImplementedError()
+
+ estimator_reacg = PyTorchClassifier(
+ model=estimator.model,
+ loss=_loss_object_pt,
+ input_shape=estimator.input_shape,
+ nb_classes=estimator.nb_classes,
+ optimizer=None,
+ channels_first=estimator.channels_first,
+ clip_values=estimator.clip_values,
+ preprocessing_defences=estimator.preprocessing_defences,
+ postprocessing_defences=estimator.postprocessing_defences,
+ preprocessing=estimator.preprocessing,
+ device_type=str(estimator._device),
+ )
+
+ else: # pragma: no cover
+ raise ValueError(f"The loss type {loss_type} is not supported for the provided estimator.")
+
+ super().__init__(estimator=estimator_reacg)
+ self.norm = norm
+ self.eps = eps
+ self.eps_step = eps_step
+ self.max_iter = max_iter
+ self.targeted = targeted
+ self.nb_random_init = nb_random_init
+ self.batch_size = batch_size
+ self.loss_type = loss_type
+ self.verbose = verbose
+ self._check_params()
+
+ def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+ """
+ Generate adversarial samples and return them in an array.
+
+ :param x: An array with the original inputs.
+ :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+ (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+ samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+ (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+ :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+ Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+ features for which the mask is zero will not be adversarially perturbed.
+ :type mask: `np.ndarray`
+ :return: An array holding the adversarial examples.
+ """
+ mask = kwargs.get("mask")
+
+ if y is not None:
+ y = check_and_transform_label_format(y, nb_classes=self.estimator.nb_classes)
+
+ if y is None:
+ if self.targeted:
+ raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+ y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size)).astype(int)
+
+ if self.estimator.nb_classes == 2 and y.shape[1] == 1:
+ raise ValueError(
+ "This attack has not yet been tested for binary classification with a single output classifier."
+ )
+
+ x_adv = x.astype(ART_NUMPY_DTYPE)
+
+ for _ in trange(max(1, self.nb_random_init), desc="ReACG - restart", disable=not self.verbose):
+ # Determine correctly predicted samples
+ y_pred = self.estimator.predict(x_adv)
+ if self.targeted:
+ sample_is_robust = np.argmax(y_pred, axis=1) != np.argmax(y, axis=1)
+ elif not self.targeted:
+ sample_is_robust = np.argmax(y_pred, axis=1) == np.argmax(y, axis=1)
+
+ if np.sum(sample_is_robust) == 0:
+ break
+
+ x_robust = x_adv[sample_is_robust]
+ y_robust = y[sample_is_robust]
+ x_init = x[sample_is_robust]
+
+ n = x_robust.shape[0]
+ m = np.prod(x_robust.shape[1:]).item()
+ random_perturbation = (
+ random_sphere(n, m, self.eps, self.norm).reshape(x_robust.shape).astype(ART_NUMPY_DTYPE)
+ )
+
+ x_robust = x_robust + random_perturbation
+
+ if self.estimator.clip_values is not None:
+ clip_min, clip_max = self.estimator.clip_values
+ x_robust = np.clip(x_robust, clip_min, clip_max)
+
+ perturbation = projection(x_robust - x_init, self.eps, self.norm)
+ x_robust = x_init + perturbation
+
+ # Compute perturbation with implicit batching
+ for batch_id in trange(
+ int(np.ceil(x_robust.shape[0] / float(self.batch_size))),
+ desc="ReACG - batch",
+ leave=False,
+ disable=not self.verbose,
+ ):
+ batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+ x_k = x_robust[batch_index_1:batch_index_2].astype(ART_NUMPY_DTYPE)
+ x_init_batch = x_init[batch_index_1:batch_index_2].astype(ART_NUMPY_DTYPE)
+ y_batch = y_robust[batch_index_1:batch_index_2]
+
+ p_0 = 0
+ p_1 = 0.43
+ var_w = [p_0, p_1]
+
+ while True:
+ p_j_p_1 = var_w[-1] + max(var_w[-1] - var_w[-2] - 0.24, 0.08)
+ if p_j_p_1 > 1:
+ break
+ var_w.append(p_j_p_1)
+
+ var_w = [math.ceil(p * self.max_iter) for p in var_w]
+
+ # self.eta = np.full((self.batch_size, 1, 1, 1), 2 * self.eps_step).astype(ART_NUMPY_DTYPE)
+ _batch_size = x_k.shape[0]
+ eta = np.full((_batch_size,) + (1,) * len(self.estimator.input_shape), self.eps_step).astype(
+ ART_NUMPY_DTYPE
+ )
+ self.count_condition_1 = np.zeros(shape=(_batch_size,))
+ gradk_1 = np.zeros_like(x_k)
+ cgradk_1 = np.zeros_like(x_k)
+ cgradk = np.zeros_like(x_k)
+ gradk_1_best = np.zeros_like(x_k)
+ cgradk_1_best = np.zeros_like(x_k)
+ gradk_1_tmp = np.zeros_like(x_k)
+ cgradk_1_tmp = np.zeros_like(x_k)
+
+ for k_iter in trange(self.max_iter, desc="ReACG - iteration", leave=False, disable=not self.verbose):
+
+ # Get perturbation, use small scalar to avoid division by 0
+ tol = 10e-8
+
+ # Get gradient wrt loss; invert it if attack is targeted
+ grad = self.estimator.loss_gradient(x_k, y_batch) * (1 - 2 * int(self.targeted))
+ if k_iter == 0:
+ gradk_1 = grad.copy()
+ cgradk_1 = grad.copy()
+ cgradk = grad.copy()
+ else:
+ beta = get_beta(grad, gradk_1, cgradk_1)
+ # Modify the coefficient beta when |beta| >> avg.(|grad / cgradk_1|)
+ _beta_normalized = get_beta(
+ grad / np.linalg.norm(grad), gradk_1 / np.linalg.norm(gradk_1), cgradk_1
+ )
+ grad_ratio_value = np.abs(grad / cgradk_1).reshape((_batch_size, -1))
+ grad_ratio = grad_ratio_value.mean(1)
+ normalize_inds = np.abs(beta).reshape((_batch_size,)) > grad_ratio
+ smaller_beta_inds = (np.abs(beta) > np.abs(_beta_normalized)).reshape((_batch_size,))
+ normalize_inds = np.logical_and(normalize_inds, smaller_beta_inds)
+ beta[normalize_inds] = _beta_normalized[normalize_inds].copy()
+ cgradk = grad + beta * cgradk_1
+
+ # Apply norm bound
+ if self.norm in [np.inf, "inf"]:
+ grad = np.sign(cgradk)
+ elif self.norm == 1:
+ ind = tuple(range(1, len(x_k.shape)))
+ cgradk = cgradk / (np.sum(np.abs(cgradk), axis=ind, keepdims=True) + tol)
+ elif self.norm == 2:
+ ind = tuple(range(1, len(x_k.shape)))
+ cgradk = cgradk / (np.sqrt(np.sum(np.square(cgradk), axis=ind, keepdims=True)) + tol)
+ assert x_k.shape == cgradk.shape
+
+ perturbation = cgradk
+
+ if mask is not None:
+ perturbation = perturbation * (mask.astype(ART_NUMPY_DTYPE))
+
+ # Apply perturbation and clip
+ x_k_p_1 = x_k + eta * perturbation
+
+ if self.estimator.clip_values is not None:
+ clip_min, clip_max = self.estimator.clip_values
+ x_k_p_1 = np.clip(x_k_p_1, clip_min, clip_max)
+
+ if k_iter == 0:
+ x_1 = x_k_p_1
+ perturbation = projection(x_1 - x_init_batch, self.eps, self.norm)
+ x_1 = x_init_batch + perturbation
+
+ f_0 = self.estimator.compute_loss(x=x_k, y=y_batch, reduction="none")
+ f_1 = self.estimator.compute_loss(x=x_1, y=y_batch, reduction="none")
+
+ self.eta_w_j_m_1 = eta.copy()
+ self.f_max_w_j_m_1 = f_0.copy()
+
+ self.f_max = f_0.copy()
+ self.x_max = x_k.copy()
+
+ f1_ge_f0 = f_1 >= f_0
+ f_1_tmp = f_1[f1_ge_f0].copy()
+ self.f_max[f1_ge_f0] = f_1_tmp.copy()
+ x_1_tmp = x_1[f1_ge_f0].copy()
+ self.x_max[f1_ge_f0] = x_1_tmp.copy()
+ self.count_condition_1[f1_ge_f0] += 1
+
+ # Settings for next iteration k
+ x_k = x_1
+ gradk_1_best = gradk_1.copy()
+ cgradk_1_best = cgradk_1.copy()
+
+ else:
+ perturbation = projection(x_k_p_1 - x_init_batch, self.eps, self.norm)
+ x_k_p_1 = x_init_batch + perturbation
+
+ if self.estimator.clip_values is not None:
+ clip_min, clip_max = self.estimator.clip_values
+ x_k_p_1 = np.clip(x_k_p_1, clip_min, clip_max)
+
+ perturbation = projection(x_k_p_1 - x_init_batch, self.eps, self.norm)
+ x_k_p_1 = x_init_batch + perturbation
+
+ f_k_p_1 = self.estimator.compute_loss(x=x_k_p_1, y=y_batch, reduction="none")
+
+ if (f_k_p_1 == 0.0).all():
+ x_k = x_k_p_1.copy()
+ break
+
+ if self.targeted:
+ fk_ge_fm = f_k_p_1 < self.f_max # assume the loss function is cross-entropy
+ else:
+ fk_ge_fm = f_k_p_1 > self.f_max
+
+ self.count_condition_1[fk_ge_fm] += 1
+ # update the best points
+ x_k_p_1_tmp = x_k_p_1[fk_ge_fm].copy()
+ self.x_max[fk_ge_fm] = x_k_p_1_tmp.copy()
+ f_k_p_1_tmp = f_k_p_1[fk_ge_fm].copy()
+ self.f_max[fk_ge_fm] = f_k_p_1_tmp.copy()
+ gradk_1_tmp = gradk_1[fk_ge_fm].copy()
+ gradk_1_best[fk_ge_fm] = gradk_1_tmp.copy()
+ cgradk_1_tmp = cgradk_1[fk_ge_fm].copy()
+ cgradk_1_best[fk_ge_fm] = cgradk_1_tmp.copy()
+
+ # update the search points
+ x_k = x_k_p_1.copy()
+ gradk_1 = grad.copy()
+ cgradk_1 = cgradk.copy()
+
+ if k_iter in var_w:
+
+ rho = 0.75
+
+ condition_1 = self.count_condition_1 < rho * (k_iter - var_w[var_w.index(k_iter) - 1])
+ condition_2 = np.logical_and(
+ (self.eta_w_j_m_1 == eta).squeeze(), self.f_max_w_j_m_1 == self.f_max
+ )
+ condition = np.logical_or(condition_1, condition_2)
+
+ # halve the stepsize if the condition is satisfied
+ eta[condition] /= 2
+ # move to the best point
+ x_max_tmp = self.x_max[condition].copy()
+ x_k[condition] = x_max_tmp.copy()
+ gradk_1_tmp = gradk_1_best[condition].copy()
+ gradk_1[condition] = gradk_1_tmp.copy()
+ cgradk_1_tmp = cgradk_1_best[condition].copy()
+ cgradk_1[condition] = cgradk_1_tmp.copy()
+
+ self.count_condition_1[:] = 0
+ self.eta_w_j_m_1 = eta.copy()
+ self.f_max_w_j_m_1 = self.f_max.copy()
+
+ y_pred_adv_k = self.estimator.predict(x_k)
+ if self.targeted:
+ sample_is_not_robust_k = np.invert(np.argmax(y_pred_adv_k, axis=1) != np.argmax(y_batch, axis=1))
+ elif not self.targeted:
+ sample_is_not_robust_k = np.invert(np.argmax(y_pred_adv_k, axis=1) == np.argmax(y_batch, axis=1))
+
+ x_robust[batch_index_1:batch_index_2][sample_is_not_robust_k] = x_k[sample_is_not_robust_k]
+
+ x_adv[sample_is_robust] = x_robust
+
+ return x_adv
+
+ def _check_params(self) -> None:
+ if self.norm not in [1, 2, np.inf, "inf"]:
+ raise ValueError('The argument norm has to be either 1, 2, np.inf, or "inf".')
+
+ if not isinstance(self.eps, (int, float)) or self.eps <= 0.0:
+ raise ValueError("The argument eps has to be either of type int or float and larger than zero.")
+
+ if not isinstance(self.eps_step, (int, float)) or self.eps_step <= 0.0:
+ raise ValueError("The argument eps_step has to be either of type int or float and larger than zero.")
+
+ if not isinstance(self.max_iter, int) or self.max_iter <= 0:
+ raise ValueError("The argument max_iter has to be of type int and larger than zero.")
+
+ if not isinstance(self.targeted, bool):
+ raise ValueError("The argument targeted has to be of bool.")
+
+ if not isinstance(self.nb_random_init, int) or self.nb_random_init <= 0:
+ raise ValueError("The argument nb_random_init has to be of type int and larger than zero.")
+
+ if not isinstance(self.batch_size, int) or self.batch_size <= 0:
+ raise ValueError("The argument batch_size has to be of type int and larger than zero.")
+
+ if not isinstance(self.verbose, bool):
+ raise ValueError("The argument `verbose` has to be of type bool.")
+
+
+def get_beta(gradk, gradk_1, cgradk_1):
+ """compute the coefficient beta required to update CG direction"""
+ _batch_size = gradk.shape[0]
+ _cgradk_1 = cgradk_1.reshape(_batch_size, -1)
+ _gradk = -gradk.reshape(_batch_size, -1)
+ _gradk_1 = -gradk_1.reshape(_batch_size, -1)
+ delta_gradk = _gradk - _gradk_1
+ betak = -(_gradk * delta_gradk).sum(axis=1) / (
+ (_cgradk_1 * delta_gradk).sum(axis=1) + np.finfo(ART_NUMPY_DTYPE).eps
+ )
+ return betak.reshape((_batch_size,) + (1,) * (len(gradk.shape) - 1))
diff --git a/art/attacks/evasion/steal_now_attack_later/__init__.py b/art/attacks/evasion/steal_now_attack_later/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/art/attacks/evasion/steal_now_attack_later/bbox_ioa.py b/art/attacks/evasion/steal_now_attack_later/bbox_ioa.py
new file mode 100644
index 0000000000..bb50c57cea
--- /dev/null
+++ b/art/attacks/evasion/steal_now_attack_later/bbox_ioa.py
@@ -0,0 +1,698 @@
+# pylint: disable=C0114
+# GNU AFFERO GENERAL PUBLIC LICENSE
+# Version 3, 19 November 2007
+#
+# Copyright (C) 2007 Free Software Foundation, Inc.
+# Everyone is permitted to copy and distribute verbatim copies
+# of this license document, but changing it is not allowed.
+#
+# Preamble
+#
+# The GNU Affero General Public License is a free, copyleft license for
+# software and other kinds of works, specifically designed to ensure
+# cooperation with the community in the case of network server software.
+#
+# The licenses for most software and other practical works are designed
+# to take away your freedom to share and change the works. By contrast,
+# our General Public Licenses are intended to guarantee your freedom to
+# share and change all versions of a program--to make sure it remains free
+# software for all its users.
+#
+# When we speak of free software, we are referring to freedom, not
+# price. Our General Public Licenses are designed to make sure that you
+# have the freedom to distribute copies of free software (and charge for
+# them if you wish), that you receive source code or can get it if you
+# want it, that you can change the software or use pieces of it in new
+# free programs, and that you know you can do these things.
+#
+# Developers that use our General Public Licenses protect your rights
+# with two steps: (1) assert copyright on the software, and (2) offer
+# you this License which gives you legal permission to copy, distribute
+# and/or modify the software.
+#
+# A secondary benefit of defending all users' freedom is that
+# improvements made in alternate versions of the program, if they
+# receive widespread use, become available for other developers to
+# incorporate. Many developers of free software are heartened and
+# encouraged by the resulting cooperation. However, in the case of
+# software used on network servers, this result may fail to come about.
+# The GNU General Public License permits making a modified version and
+# letting the public access it on a server without ever releasing its
+# source code to the public.
+#
+# The GNU Affero General Public License is designed specifically to
+# ensure that, in such cases, the modified source code becomes available
+# to the community. It requires the operator of a network server to
+# provide the source code of the modified version running there to the
+# users of that server. Therefore, public use of a modified version, on
+# a publicly accessible server, gives the public access to the source
+# code of the modified version.
+#
+# An older license, called the Affero General Public License and
+# published by Affero, was designed to accomplish similar goals. This is
+# a different license, not a version of the Affero GPL, but Affero has
+# released a new version of the Affero GPL which permits relicensing under
+# this license.
+#
+# The precise terms and conditions for copying, distribution and
+# modification follow.
+#
+# TERMS AND CONDITIONS
+#
+# 0. Definitions.
+#
+# "This License" refers to version 3 of the GNU Affero General Public License.
+#
+# "Copyright" also means copyright-like laws that apply to other kinds of
+# works, such as semiconductor masks.
+#
+# "The Program" refers to any copyrightable work licensed under this
+# License. Each licensee is addressed as "you". "Licensees" and
+# "recipients" may be individuals or organizations.
+#
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+# exact copy. The resulting work is called a "modified version" of the
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+#
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+# otherwise be available to you under applicable patent law.
+#
+# 12. No Surrender of Others' Freedom.
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+# not convey it at all. For example, if you agree to terms that obligate you
+# to collect a royalty for further conveying from those to whom you convey
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+#
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+# ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
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+# THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+# GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
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+# SUCH DAMAGES.
+#
+# 17. Interpretation of Sections 15 and 16.
+#
+# If the disclaimer of warranty and limitation of liability provided
+# above cannot be given local legal effect according to their terms,
+# reviewing courts shall apply local law that most closely approximates
+# an absolute waiver of all civil liability in connection with the
+# Program, unless a warranty or assumption of liability accompanies a
+# copy of the Program in return for a fee.
+#
+# END OF TERMS AND CONDITIONS
+#
+# How to Apply These Terms to Your New Programs
+#
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+# possible use to the public, the best way to achieve this is to make it
+# free software which everyone can redistribute and change under these terms.
+#
+# To do so, attach the following notices to the program. It is safest
+# to attach them to the start of each source file to most effectively
+# state the exclusion of warranty; and each file should have at least
+# the "copyright" line and a pointer to where the full notice is found.
+#
+#
+# Copyright (C)
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program. If not, see .
+#
+# Also add information on how to contact you by electronic and paper mail.
+#
+# If your software can interact with users remotely through a computer
+# network, you should also make sure that it provides a way for users to
+# get its source. For example, if your program is a web application, its
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+# of the code. There are many ways you could offer source, and different
+# solutions will be better for different programs; see section 13 for the
+# specific requirements.
+#
+# You should also get your employer (if you work as a programmer) or school,
+# if any, to sign a "copyright disclaimer" for the program, if necessary.
+# For more information on this, and how to apply and follow the GNU AGPL, see
+# .
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+ import torch
+
+
+def bbox_ioa(box1: "torch.Tensor", box2: "torch.Tensor", eps: float = 1e-7) -> "torch.Tensor":
+ """
+ === NOTE ===
+ This function is copied from YOLOv5 repository (yolov5/utils/metrics.py)
+ === ==== ===
+ Calculate the intersection over two boxes represented by the format x1y1x2y2.
+
+ :param box1: The first box.
+ :param box2: The second box.
+
+ :return: Intersection over box2 area
+ """
+
+ # Get the coordinates of bounding boxes
+ b1_x1, b1_y1, b1_x2, b1_y2 = box1
+ b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
+
+ # Intersection area
+ inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * (
+ np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)
+ ).clip(0)
+
+ # box2 area
+ box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
+
+ # Intersection over box2 area
+ return inter_area / box2_area
diff --git a/art/attacks/evasion/steal_now_attack_later/drop_block2d.py b/art/attacks/evasion/steal_now_attack_later/drop_block2d.py
new file mode 100644
index 0000000000..29fecf3414
--- /dev/null
+++ b/art/attacks/evasion/steal_now_attack_later/drop_block2d.py
@@ -0,0 +1,70 @@
+# pylint: disable=C0114
+# BSD 3-Clause License
+#
+# Copyright (c) Soumith Chintala 2016,
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+ import torch
+
+
+def drop_block2d(x: "torch.Tensor", prob: float, block_size: int):
+ """
+ === NOTE ===
+ This function is modified from torchvision (torchvision/ops/drop_block.py)
+ BSD 3-Clause License
+ === ==== ===
+ :param x (Tensor[N, C, H, W]): The input tensor or 4-dimensions with the first one
+ being its batch i.e. a batch with ``N`` rows.
+ :param prob (float): Probability of an element to be dropped.
+ :param block_size (int): Size of the block to drop.
+
+ :return: Tensor[N, C, H, W]: The mask of activate pixels.
+ """
+ import torch
+
+ if prob < 0.0 or prob > 1.0:
+ raise ValueError(f"drop probability has to be between 0 and 1, but got {prob}.")
+ if x.ndim != 4:
+ raise ValueError(f"input should be 4 dimensional. Got {x.ndim} dimensions.")
+
+ N, _, H, W = x.size() # pylint: disable=C0103
+ block_size = min(block_size, W, H)
+ # compute the gamma of Bernoulli distribution
+ gamma = (prob * H * W) / ((block_size**2) * ((H - block_size + 1) * (W - block_size + 1)))
+ noise = torch.empty((N, 1, H - block_size + 1, W - block_size + 1), dtype=x.dtype, device=x.device)
+ noise.bernoulli_(gamma)
+
+ noise = torch.nn.functional.pad(noise, [block_size // 2] * 4, value=0)
+ noise = torch.nn.functional.max_pool2d(
+ noise, stride=(1, 1), kernel_size=(block_size, block_size), padding=block_size // 2
+ )
+ mask = 1 - noise
+ return mask
diff --git a/art/attacks/evasion/steal_now_attack_later/steal_now_attack_later.py b/art/attacks/evasion/steal_now_attack_later/steal_now_attack_later.py
new file mode 100644
index 0000000000..4dfe47f770
--- /dev/null
+++ b/art/attacks/evasion/steal_now_attack_later/steal_now_attack_later.py
@@ -0,0 +1,734 @@
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2024
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+"""
+This module implements the paper: Steal Now and Attack Later: Evaluating Robustness of Object Detection against
+Black-box Adversarial Attacks
+
+| Paper link: https://arxiv.org/abs/2304.05370
+"""
+
+# pylint: disable=C0302
+
+import logging
+import random
+from typing import Callable, Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.attacks.attack import EvasionAttack
+from art.attacks.evasion.steal_now_attack_later.bbox_ioa import bbox_ioa
+from art.attacks.evasion.steal_now_attack_later.drop_block2d import drop_block2d
+
+if TYPE_CHECKING:
+ # pylint: disable=C0412
+ import torch
+ from art.utils import PYTORCH_OBJECT_DETECTOR_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+# tiling
+def _generate_tile_kernel(patch: list, mask: list, tile_size: int) -> Tuple["torch.Tensor", "torch.Tensor"]:
+ """
+ Generate specific size of pertuerbed tiles from randomly selected patches.
+
+ :param patch: Candiate patches.
+ :param mask: Masks for each patch.
+ :param tile_size: The size of each tile.
+ :return: Pertuerbed tiles and corresponding maskes.
+ """
+ import torch
+ import torchvision
+
+ idx_seq = list(range(len(patch)))
+ target = random.sample(idx_seq, k=1)[0]
+ t_patch = patch[target]
+ t_mask = mask[target]
+ if t_mask is None:
+ t_mask = torch.ones_like(t_patch)
+ width, height = t_patch.shape[-2], t_patch.shape[-1]
+ boundary = 1
+ tile_size = max(tile_size - 2 * boundary, 1)
+
+ if height > width:
+ flip = True
+ FlipOp = torchvision.transforms.RandomVerticalFlip(0.2) # pylint: disable=C0103
+ max_len = height
+ min_len = width
+ t_patch = torch.permute(t_patch, (0, 2, 1))
+ t_mask = torch.permute(t_mask, (0, 2, 1))
+ else:
+ flip = False
+ FlipOp = torchvision.transforms.RandomHorizontalFlip(0.2) # pylint: disable=C0103
+ max_len = width
+ min_len = height
+
+ if max_len > tile_size:
+ new_len = round(min_len * tile_size / max_len)
+ p_1 = torchvision.transforms.Resize((tile_size, new_len))(t_patch)
+ # fix for the case that (strides - new_len) > new_len
+ p_list = []
+
+ for _ in range(tile_size // new_len):
+ p_list.append(FlipOp(p_1))
+
+ p_2 = torchvision.transforms.RandomCrop((tile_size, tile_size % new_len))(p_1)
+ p_list.append(FlipOp(p_2))
+
+ n_patch = torch.cat(p_list, dim=-1)
+ n_patch = torchvision.transforms.CenterCrop((tile_size + 2 * boundary, tile_size + 2 * boundary))(n_patch)
+ n_mask = torch.where(n_patch == 0, torch.zeros_like(n_patch), torch.ones_like(n_patch))
+
+ elif max_len >= tile_size / 2.0:
+ new_len = round(min_len * (tile_size / 2.0) / max_len)
+
+ p_list = []
+ for _ in range(tile_size // new_len):
+ repeat = 2
+ p1_list = []
+ for _ in range(repeat):
+ p_1 = torchvision.transforms.Resize((tile_size // 2, new_len))(t_patch)
+ if torch.rand([]) < 0.6:
+ p1_list.append(FlipOp(p_1))
+ else:
+ p1_list.append(torch.zeros_like(p_1))
+ p_1 = torch.cat(p1_list, dim=-2)
+ p_list.append(p_1)
+
+ p_2 = torchvision.transforms.RandomCrop((tile_size, tile_size % new_len))(p_1)
+ p_list.append(FlipOp(p_2))
+
+ n_patch = torch.cat(p_list, dim=-1)
+ n_patch = torchvision.transforms.CenterCrop((tile_size + 2 * boundary, tile_size + 2 * boundary))(n_patch)
+ n_mask = torch.where(n_patch == 0, torch.zeros_like(n_patch), torch.ones_like(n_patch))
+
+ else:
+ t_1 = torch.cat([t_patch[None, :], t_mask[None, :]], dim=0)
+ p_list = []
+ n_list = []
+ for _ in range(tile_size // min_len):
+ p1_list = []
+ m1_list = []
+ for _ in range(tile_size // max_len):
+ if torch.rand([]) < 0.4:
+ t_1 = FlipOp(t_1)
+ p1_list.append(t_1[0, :])
+ m1_list.append(t_1[1, :])
+ else:
+ p1_list.append(torch.zeros_like(t_patch))
+ m1_list.append(torch.zeros_like(t_mask))
+ p_1 = torch.cat(p1_list, dim=-2)
+ m_1 = torch.cat(m1_list, dim=-2)
+ p_list.append(p_1)
+ n_list.append(m_1)
+ n_patch = torch.cat(p_list, dim=-1)
+ n_mask = torch.cat(n_list, dim=-1)
+ n_patch = torchvision.transforms.CenterCrop((tile_size + 2 * boundary, tile_size + 2 * boundary))(n_patch)
+ n_mask = torchvision.transforms.CenterCrop((tile_size + 2 * boundary, tile_size + 2 * boundary))(n_mask)
+
+ if flip:
+ n_patch = torch.permute(n_patch, (0, 2, 1))
+ n_mask = torch.permute(n_mask, (0, 2, 1))
+
+ return n_patch, n_mask
+
+
+def generate_tile(patches: list, masks: list, tile_size: int, scale: list) -> Tuple["torch.Tensor", "torch.Tensor"]:
+ """
+ Generate different size of pertuerbed tiles from randomly selected patches.
+
+ :param patch: Candiate patches.
+ :param mask: Masks for each patch.
+ :param tile_size: The size of each tile.
+ :param scale: Scale factor for various tileing size.
+ :return: Pertuerbed tiles and corresponding maskes.
+ """
+ import torch
+
+ if len(patches) == 0:
+ raise ValueError("candidates should not be empty.")
+ device = patches[0].device
+
+ tile = torch.zeros((0, 3, tile_size, tile_size), device=device)
+ mask = torch.zeros((0, 3, tile_size, tile_size), device=device)
+ for cur_s in scale:
+ cur_strides = tile_size // cur_s
+ cur_tile = []
+ cur_mask = []
+
+ for _ in range(cur_s):
+ t1_list = []
+ m1_list = []
+ for _ in range(cur_s):
+ g_tile, f_mask = _generate_tile_kernel(patches, masks, tile_size=cur_strides)
+ t1_list.append(g_tile[None, :])
+ m1_list.append(f_mask[None, :])
+ cur_t = torch.cat(t1_list, dim=-2)
+ cur_m = torch.cat(m1_list, dim=-2)
+ cur_tile.append(cur_t)
+ cur_mask.append(cur_m)
+ cur_tile = torch.cat(cur_tile, dim=-1) # type: ignore
+ cur_mask = torch.cat(cur_mask, dim=-1) # type: ignore
+
+ tile = torch.cat([tile, cur_tile], dim=0) # type: ignore
+ mask = torch.cat([mask, cur_mask], dim=0) # type: ignore
+
+ return tile, mask
+
+
+class TileObj:
+ """
+ Internally used object that stores information about each tile.
+ """
+
+ def __init__(self, tile_size: int, device: "torch.device") -> None:
+ """
+ Create a tile instance.
+ """
+ import torch
+
+ self.patch = torch.zeros((3, tile_size, tile_size), device=device)
+ self.diff = torch.ones([], device=device) * self.patch.shape.numel()
+ self.bcount = 0
+ self.eligible = False
+
+ def update(self, eligible=None, bcount=None, diff=None, patch=None) -> None:
+ """
+ Update the properties of the object
+ """
+ if eligible is not None:
+ self.eligible = eligible
+
+ if bcount is not None:
+ self.bcount = bcount
+
+ if diff is not None:
+ self.diff = diff
+
+ if patch is not None:
+ self.patch = patch
+
+ def compare(self, target: "TileObj") -> bool:
+ """
+ Comparison operation.
+ """
+
+ if self.eligible is True and target.eligible is False:
+ return True
+
+ if self.eligible is False and target.eligible is True:
+ return False
+
+ if self.bcount > target.bcount:
+ return True
+ if self.bcount < target.bcount:
+ return False
+
+ return bool(self.diff < target.diff)
+
+
+class TileArray:
+ """
+ Internally used object that stores the list of tiles.
+ """
+
+ def __init__(self, xyxy: list, threshold: int, tile_size: int, k: int, device: "torch.device") -> None:
+ """
+ Initialization operation.
+ """
+ import torch
+
+ self.threshold = threshold
+ self.tile_size = tile_size
+ self.device = device
+ self.xyxy = torch.Tensor(xyxy).to(device)
+ self.k = k
+ self.patch_list = [TileObj(tile_size=tile_size, device=device)] * self.k
+
+ def insert(self, target: TileObj) -> None:
+ """
+ Insertion operation.
+ """
+ if target.bcount < self.threshold:
+ return
+
+ prev = self.patch_list
+ out = []
+ for k_it in range(self.k):
+ if target.compare(prev[k_it]):
+ out.append(target)
+ out = out + prev[k_it:]
+ break
+
+ out.append(prev[k_it])
+
+ self.patch_list = out[: self.k]
+
+ def pop(self) -> None:
+ """
+ Pop operation.
+ """
+ out = self.patch_list[1:] + [TileObj(tile_size=self.tile_size, device=self.device)]
+ self.patch_list = out
+
+
+class SNAL(EvasionAttack):
+ """
+ Steal Now and Attack Later
+
+ | Paper link: https://arxiv.org/abs/2404.15881
+ """
+
+ attack_params = EvasionAttack.attack_params + [
+ "eps",
+ "max_iter",
+ "num_grid",
+ "batch_size",
+ ]
+
+ _estimator_requirements = ()
+
+ def __init__(
+ self,
+ estimator: "PYTORCH_OBJECT_DETECTOR_TYPE",
+ candidates: list,
+ collector: Callable,
+ eps: float,
+ max_iter: int,
+ num_grid: int,
+ ) -> None:
+ """
+ Create a SNAL attack instance.
+
+ :param estimator: A trained YOLOv8 model or other models with the same output format
+ :param candidates: The collected pateches to generate perturbations.
+ :param collector: A callbel uses to generate patches.
+ :param eps: Maximum perturbation that the attacker can introduce.
+ :param max_iter: The maximum number of iterations.
+ :param num_grid: The number of grids for width and high dimension.
+ """
+ super().__init__(estimator=estimator)
+ self.eps = eps
+ self.max_iter = max_iter
+ self.num_grid = num_grid
+ self.batch_size = 1
+ self.candidates = candidates
+ self.threshold_objs = 1 # the expect number of objects
+ self.collector = collector
+ self._check_params()
+
+ def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+ """
+ Generate adversarial samples and return them in an array.
+
+ :param x: An array with the original inputs to be attacked.
+ :param y: Not used.
+ :return: An array holding the adversarial examples.
+ """
+
+ # Compute adversarial examples with implicit batching
+ x_adv = x.copy()
+ for batch_id in range(int(np.ceil(x_adv.shape[0] / float(self.batch_size)))):
+ batch_index_1 = batch_id * self.batch_size
+ batch_index_2 = min((batch_id + 1) * self.batch_size, x_adv.shape[0])
+ x_batch = x_adv[batch_index_1:batch_index_2]
+ x_adv[batch_index_1:batch_index_2] = self._generate_batch(x_batch)
+
+ return x_adv
+
+ def _generate_batch(
+ self, x_batch: np.ndarray, y_batch: Optional[np.ndarray] = None # pylint: disable=W0613
+ ) -> np.ndarray:
+ """
+ Run the attack on a batch of images.
+
+ :param x_batch: A batch of original examples.
+ :param y_batch: Not Used.
+ :return: A batch of adversarial examples.
+ """
+ import torch
+
+ x_org = torch.from_numpy(x_batch).to(self.estimator.device)
+ x_adv = x_org.clone()
+
+ cond = torch.logical_or(x_org < 0.0, x_org > 1.0)
+ if torch.any(cond):
+ raise ValueError("The value of each pixel must be normalized in the range [0, 1].")
+
+ x_adv = self._attack(x_adv, x_org)
+
+ return x_adv.cpu().detach().numpy()
+
+ def _attack(self, x_adv: "torch.Tensor", x: "torch.Tensor") -> "torch.Tensor":
+ """
+ Run attack.
+
+ :param x_batch: A batch of original examples.
+ :param y_batch: Not Used.
+ :return: A batch of adversarial examples.
+ """
+ import torch
+
+ if self.candidates is None:
+ raise ValueError("A set of patches should be collected before executing the attack.")
+
+ if x.shape[-1] % self.num_grid != 0 or x.shape[-2] % self.num_grid != 0:
+ raise ValueError("The size of the image must be divided by the number of grids")
+ tile_size = x.shape[-1] // self.num_grid
+
+ # Prapare a 2D array to store the results of each grid
+ buffer_depth = 5
+ tile_mat = {}
+ for idx_i in range(self.num_grid):
+ for idx_j in range(self.num_grid):
+ x_1 = idx_i * tile_size
+ y_1 = idx_j * tile_size
+ x_2 = x_1 + tile_size
+ y_2 = y_1 + tile_size
+ tile_mat[(idx_i, idx_j)] = TileArray(
+ list([x_1, y_1, x_2, y_2]), self.threshold_objs, tile_size, buffer_depth, self.estimator.device
+ )
+
+ # init guess
+ n_samples = 10
+ x_adv, tile_mat = self._init_guess(tile_mat, x_adv, x, tile_size, n_samples=n_samples)
+
+ batch_idx = 0
+ candidates_patch = self.candidates
+ candidates_mask = [None] * len(candidates_patch)
+
+ r_tile = torch.zeros((0, 3, tile_size, tile_size), device=self.estimator.device)
+ r_mask = torch.zeros((0, 3, tile_size, tile_size), device=self.estimator.device)
+ while r_tile.shape[0] < n_samples:
+ t_tile, t_mask = generate_tile(candidates_patch, candidates_mask, tile_size, [1, 2])
+ r_tile = torch.cat([r_tile, t_tile], dim=0)
+ r_mask = torch.cat([r_mask, t_mask], dim=0)
+
+ for _ in range(self.max_iter):
+ adv_patch, adv_position = self.collector(self.estimator, x_adv)
+ adv_position = adv_position[0]
+ candidates_patch = candidates_patch + adv_patch[0]
+ candidates_mask = candidates_mask + [None] * len(adv_patch[0])
+
+ for key, obj in tile_mat.items():
+ idx_i, idx_j = key
+ box_1 = obj.xyxy
+ obj_threshold = obj.threshold
+ [x_1, y_1, x_2, y_2] = box_1.type(torch.IntTensor) # type: ignore
+ overlay = bbox_ioa(box_1.type(torch.FloatTensor), adv_position.type(torch.FloatTensor)) # type: ignore
+ bcount = torch.sum(overlay > 0.0).item()
+
+ pert = x_adv[batch_idx, :, y_1:y_2, x_1:x_2] - x[batch_idx, :, y_1:y_2, x_1:x_2]
+ loss = self._get_loss(pert, self.eps)
+ eligible = torch.max(torch.abs(pert)) < self.eps and bcount >= obj_threshold
+ tpatch_cur = TileObj(tile_size=tile_size, device=self.estimator.device)
+ tpatch_cur.update(eligible, bcount, torch.sum(loss), x_adv[batch_idx, :, y_1:y_2, x_1:x_2].clone())
+
+ # insert op
+ prev = tile_mat[(idx_i, idx_j)]
+ prev.insert(tpatch_cur)
+ tile_mat[(idx_i, idx_j)] = prev
+
+ sorted_patch = tile_mat[(idx_i, idx_j)].patch_list
+ bcount_list = []
+ for cur_sp in sorted_patch:
+ if cur_sp.bcount >= obj_threshold:
+ bcount_list.append(cur_sp)
+
+ if len(bcount_list) == buffer_depth and bcount_list[-1].bcount > obj_threshold:
+ tile_mat[(idx_i, idx_j)].threshold = obj_threshold + 1
+
+ if len(bcount_list) < buffer_depth:
+
+ while r_tile.shape[0] < int(1.5 * n_samples):
+ t_tile, t_mask = generate_tile(candidates_patch, candidates_mask, tile_size, [1, 2])
+ r_tile = torch.cat([r_tile, t_tile], dim=0)
+ r_mask = torch.cat([r_mask, t_mask], dim=0)
+
+ # select n_sample candidates
+ c_tile = r_tile
+ idx_perm = torch.randperm(c_tile.shape[0])
+ idx_perm = idx_perm[:n_samples]
+ c_tile = r_tile[idx_perm, :]
+ c_mask = r_mask[idx_perm, :]
+ x_ref = x[:, :, y_1:y_2, x_1:x_2]
+
+ updated = ((1.0 - c_mask) * x_ref) + c_mask * (0.0 * x_ref + 1.0 * c_tile)
+
+ n_mask = drop_block2d(c_mask, 0.05, 1)
+ updated = (1.0 - n_mask) * x_ref + n_mask * updated
+ pert = updated - x_ref
+
+ loss = torch.sum(self._get_loss(pert, self.eps), dim=(1, 2, 3))
+ min_idx = torch.min(loss, dim=0).indices.item()
+ updated = updated[min_idx, :]
+ updated = updated[None, :]
+
+ else:
+ target = bcount_list[0].patch[None, :]
+ x_ref = x[batch_idx, :, y_1:y_2, x_1:x_2]
+ updated = self._color_projection(target, x_ref, self.eps)
+
+ x_adv[batch_idx, :, y_1:y_2, x_1:x_2] = updated
+ x_adv = torch.round(x_adv * 255.0) / 255.0
+ x_adv = torch.clamp(x_adv, x - 2.5 * self.eps, x + 2.5 * self.eps)
+ x_adv = torch.clamp(x_adv, 0.0, 1.0)
+
+ x_out = self._assemble(tile_mat, x)
+ mask = torch.zeros_like(x_out)
+ _, adv_position = self.collector(self.estimator, x_out)
+ for pos in adv_position[0]:
+ mask[:, :, pos[1] : pos[3], pos[0] : pos[2]] = mask[:, :, pos[1] : pos[3], pos[0] : pos[2]] + 1
+ mask = torch.where(mask > 0, torch.ones_like(mask), torch.zeros_like(mask))
+ x_adv = mask * x_out + (1.0 - mask) * x
+ x_adv = torch.clamp(x_adv, x - self.eps, x + self.eps)
+ x_adv = torch.clamp(x_adv, 0.0, 1.0)
+
+ return x_adv
+
+ def _get_loss(self, pert: "torch.Tensor", epsilon: float) -> "torch.Tensor": # pylint: disable=R0201
+ """
+ Calculate accumulated distance of the perturbations outside the epslion ball.
+
+ :param pert: Perturbations in the pixel space.
+ :param epsilon: The radius of the eplion bass.
+ :return: loss.
+ """
+ import torch
+
+ count = torch.where(pert == 0, torch.zeros_like(pert), torch.ones_like(pert))
+ pert = torch.where(torch.abs(pert) <= epsilon, torch.zeros_like(pert), pert)
+ pert = torch.abs(pert)
+ loss = torch.sqrt(pert) / torch.sum(count)
+
+ return loss
+
+ def _color_projection( # pylint: disable=R0201
+ self, tile: "torch.Tensor", x_ref: "torch.Tensor", epsilon: float
+ ) -> "torch.Tensor":
+ """
+ Convert statistics information from target to source.
+
+ :param tile: The target to convert.
+ :param x_ref: The source data.
+ :param epsilon: The radius of the eplion bass.
+ :return: The converted tile.
+ """
+ import torch
+
+ if len(tile.shape) == 3:
+ tile = tile[None, :]
+ if len(x_ref.shape) == 3:
+ x_ref = x_ref[None, :]
+
+ pert = tile - x_ref
+ cond = torch.abs(pert) > epsilon
+ sign = (torch.rand_like(pert) - 0.5) * 2
+
+ u_bound = torch.max(pert, torch.ones_like(pert) * epsilon)
+ l_bound = torch.min(pert, torch.ones_like(pert) * -epsilon)
+ set1 = torch.where(sign > 0, 0.5 * pert, pert - torch.sign(pert) * epsilon)
+ set1 = torch.clamp(set1, l_bound, u_bound)
+ set1 = set1 + x_ref
+
+ set2 = tile
+ mean_s = torch.mean(x_ref, dim=(-2, -1), keepdim=True)
+ mean_t = torch.mean(x_ref, dim=(-2, -1), keepdim=True)
+ std_s = torch.std(set2, dim=(-2, -1), keepdim=True)
+ std_t = torch.std(set2, dim=(-2, -1), keepdim=True)
+ scale = std_s / std_t
+ set2 = (set2 - mean_t) * scale + mean_s
+ set2 = torch.clamp(set2, 0.0, 1.0)
+
+ set2 = set2 + sign * epsilon * scale
+ set2 = torch.clamp(set2, 0, 1)
+
+ updated = torch.where(cond, set1, set2)
+
+ return updated
+
+ def _assemble(self, tile_mat: dict, x_org: "torch.Tensor") -> "torch.Tensor": # pylint: disable=R0201
+ """
+ Combine the best patches from each grid into a single image.
+
+ :param tile_mat: Internal structure used to store patches for each mesh.
+ :param x_org: The original images.
+ :return: Perturbed images.
+ """
+ import torch
+
+ ans = x_org.clone()
+ for obj in tile_mat.values():
+ [x_1, y_1, x_2, y_2] = obj.xyxy.type(torch.IntTensor)
+ tile = obj.patch_list[0].patch[None, :]
+ mask = torch.where(tile != 0, torch.ones_like(tile), torch.zeros_like(tile))
+ ans[0, :, y_1:y_2, x_1:x_2] = mask * tile + (1.0 - mask) * ans[0, :, y_1:y_2, x_1:x_2]
+ return ans
+
+ def _init_guess(
+ self, tile_mat: dict, x_init: "torch.Tensor", x_org: "torch.Tensor", tile_size: int, n_samples: int
+ ) -> Tuple["torch.Tensor", dict]:
+ """
+ Generate an initial perturbation for each grid.
+
+ :param tile_mat: Internal structure used to store patches for each mesh.
+ :param x_init: Perturbed images from previous runs.
+ :param x_org: The original images.
+ :param tile_size: The size of each tile.
+ :return: Guessed images and internal structure.
+ """
+ import torch
+
+ TRIAL = 10 # pylint: disable=C0103
+ patches = self.candidates
+ masks = [None] * len(self.candidates)
+ for _ in range(TRIAL):
+ x_cand = torch.zeros(
+ (n_samples, 3, x_init.shape[-2], x_init.shape[-1]), dtype=x_init.dtype, device=self.estimator.device
+ )
+
+ # generate tiles
+ # To save the computing time, we generate some tiles in advance.
+ # partial tiles are updated on-the-fly
+ r_tile = torch.zeros((0, 3, tile_size, tile_size), device=self.estimator.device)
+ r_mask = torch.zeros((0, 3, tile_size, tile_size), device=self.estimator.device)
+ while r_tile.shape[0] < n_samples:
+ t_tile, t_mask = generate_tile(patches, masks, tile_size, [1, 2])
+ r_tile = torch.cat([r_tile, t_tile], dim=0)
+ r_mask = torch.cat([r_mask, t_mask], dim=0)
+
+ for _, obj in tile_mat.items():
+ # select n_samples
+ while r_tile.shape[0] < int(1.5 * n_samples):
+ t_tile, t_mask = generate_tile(patches, masks, tile_size, [1, 2])
+ r_tile = torch.cat([r_tile, t_tile], dim=0)
+ r_mask = torch.cat([r_mask, t_mask], dim=0)
+
+ idx_perm = torch.randperm(r_tile.shape[0])
+ idx_perm = idx_perm[:n_samples]
+ tile_perm = r_tile[idx_perm, :]
+ mask_perm = r_mask[idx_perm, :]
+
+ # merge tiles
+ box_1 = obj.xyxy
+ [x_1, y_1, x_2, y_2] = box_1.type(torch.IntTensor)
+ x_ref = x_init[:, :, y_1:y_2, x_1:x_2]
+ x_new = ((1.0 - mask_perm) * x_ref) + mask_perm * (0.0 * x_ref + 1.0 * tile_perm)
+
+ # randomly roll-back
+ rand_rb = torch.rand([n_samples, 1, 1, 1], device=self.estimator.device)
+ x_new = torch.where(rand_rb < 0.8, x_new, x_ref)
+ x_cand[:, :, y_1:y_2, x_1:x_2] = x_new
+
+ # spatial drop
+ n_mask = drop_block2d(x_cand, 0.05, 3)
+ x_cand = (1.0 - n_mask) * x_org + n_mask * x_cand
+ # x_cand = smooth_image(x_cand, x_org, epsilon, 10)
+ x_cand = torch.round(x_cand * 255.0) / 255.0
+ x_cand = torch.clamp(x_cand, x_org - 2.5 * self.eps, x_org + 2.5 * self.eps)
+ x_cand = torch.clamp(x_cand, 0.0, 1.0)
+
+ # update results
+ _, adv_position = self.collector(self.estimator, x_cand)
+ for idx in range(n_samples):
+ cur_position = adv_position[idx]
+
+ for key, obj in tile_mat.items():
+
+ idx_i, idx_j = key
+ box_1 = obj.xyxy
+ obj_threshold = obj.threshold
+ [x_1, y_1, x_2, y_2] = box_1.type(torch.IntTensor)
+ overlay = bbox_ioa(box_1.type(torch.FloatTensor), cur_position.type(torch.FloatTensor))
+ bcount = torch.sum(overlay > 0.0).item()
+
+ x_ref = x_org[:, :, y_1:y_2, x_1:x_2]
+ x_cur = x_cand[idx, :, y_1:y_2, x_1:x_2].clone()
+
+ pert = x_cur - x_ref
+ loss = self._get_loss(pert, self.eps)
+ eligible = torch.max(torch.abs(pert)) < self.eps and bcount >= obj_threshold
+ tpatch_cur = TileObj(tile_size=tile_size, device=self.estimator.device)
+ tpatch_cur.update(eligible, bcount, torch.sum(loss), x_cur)
+ # insert op
+ prev = tile_mat[(idx_i, idx_j)]
+ prev.insert(tpatch_cur)
+ tile_mat[(idx_i, idx_j)] = prev
+
+ # clean non-active regions
+ x_out = x_init.clone()
+ x_eval = self._assemble(tile_mat, x_org)
+ _, adv_position = self.collector(self.estimator, x_eval)
+ cur_position = adv_position[0]
+ for key, obj in tile_mat.items():
+ idx_i, idx_j = key
+ box_1 = obj.xyxy
+ [x_1, y_1, x_2, y_2] = box_1.type(torch.IntTensor)
+ overlay = bbox_ioa(box_1.type(torch.FloatTensor), cur_position.type(torch.FloatTensor))
+ bcount = torch.sum(overlay > 0.0).item()
+
+ x_ref = x_init[:, :, y_1:y_2, x_1:x_2]
+ x_tag = x_eval[:, :, y_1:y_2, x_1:x_2]
+ cur_mask = torch.zeros_like(x_ref)
+ if bcount > 1:
+ bbox = cur_position[overlay > 0.0]
+ for box in bbox:
+ bx1 = torch.clamp_min(box[0] - x_1, 0)
+ by1 = torch.clamp_min(box[1] - y_1, 0)
+ bx2 = torch.clamp_max(box[2] - x_1, (x_2 - x_1 - 1).to(self.estimator.device))
+ by2 = torch.clamp_max(box[3] - y_1, (y_2 - y_1 - 1).to(self.estimator.device))
+ cur_mask[:, :, by1:by2, bx1:bx2] = 1.0
+ else:
+ prev = tile_mat[(idx_i, idx_j)]
+ prev.pop()
+ tile_mat[(idx_i, idx_j)] = prev
+
+ a_mask = drop_block2d(x_ref, 0.05, 1)
+ cur_mask = cur_mask * a_mask
+ updated = ((1.0 - cur_mask) * x_ref) + cur_mask * (0.0 * x_ref + 1.0 * x_tag)
+ updated = ((1.0 - cur_mask) * x_ref) + cur_mask * (0.0 * x_ref + 1.0 * updated)
+
+ x_out[:, :, y_1:y_2, x_1:x_2] = updated
+
+ return x_out, tile_mat
+
+ def _check_params(self) -> None:
+
+ if not isinstance(self.eps, float):
+ raise TypeError("The eps has to be of type float.")
+
+ if self.eps < 0 or self.eps > 1:
+ raise ValueError("The eps must be in the range [0, 1].")
+
+ if not isinstance(self.max_iter, int):
+ raise TypeError("The max_iter has to be of type int.")
+
+ if self.max_iter < 1:
+ raise ValueError("The number of iterations must be a positive integer.")
+
+ if not isinstance(self.num_grid, int):
+ raise TypeError("The num_grid has to be of type int.")
+
+ if self.num_grid < 1:
+ raise ValueError("The number of grid must be a positive integer.")
+
+ if not isinstance(self.candidates, list):
+ raise TypeError("Candidates must be stored in list.")
+
+ if len(self.candidates) < 1:
+ raise ValueError("The list of candidates is empty.")
diff --git a/art/defences/detector/evasion/__init__.py b/art/defences/detector/evasion/__init__.py
index 26112a2afe..2a08f29e7a 100644
--- a/art/defences/detector/evasion/__init__.py
+++ b/art/defences/detector/evasion/__init__.py
@@ -6,3 +6,4 @@
from art.defences.detector.evasion.binary_input_detector import BinaryInputDetector
from art.defences.detector.evasion.binary_activation_detector import BinaryActivationDetector
from art.defences.detector.evasion.subsetscanning.detector import SubsetScanningDetector
+from art.defences.detector.evasion.beyond_detector import BeyondDetectorPyTorch
diff --git a/art/defences/detector/evasion/beyond_detector.py b/art/defences/detector/evasion/beyond_detector.py
new file mode 100644
index 0000000000..e48c056ea6
--- /dev/null
+++ b/art/defences/detector/evasion/beyond_detector.py
@@ -0,0 +1,185 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2024
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the BEYOND detector for adversarial examples detection.
+
+| Paper link: https://openreview.net/pdf?id=S4LqI6CcJ3
+"""
+from __future__ import annotations
+
+import math
+from typing import TYPE_CHECKING, Callable
+
+import numpy as np
+
+if TYPE_CHECKING:
+ import torch
+ from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+
+from art.defences.detector.evasion.evasion_detector import EvasionDetector
+
+
+class BeyondDetectorPyTorch(EvasionDetector):
+ """
+ BEYOND detector for adversarial samples detection.
+ This detector uses a combination of SSL and target model predictions to detect adversarial examples.
+
+ | Paper link: https://openreview.net/pdf?id=S4LqI6CcJ3
+ """
+
+ defence_params = ["target_model", "ssl_model", "augmentations", "aug_num", "alpha", "var_K", "percentile"]
+
+ def __init__(
+ self,
+ target_classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+ ssl_classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+ augmentations: Callable,
+ aug_num: int = 50,
+ alpha: float = 0.8,
+ var_K: int = 20,
+ percentile: int = 5,
+ ) -> None:
+ """
+ Initialize the BEYOND detector.
+
+ :param target_classifier: The target model to be protected
+ :param ssl_classifier: The self-supervised learning model used for feature extraction
+ :param augmentations: data augmentations for generating neighborhoods
+ :param aug_num: Number of augmentations to apply to each sample (default: 50)
+ :param alpha: Weight factor for combining label and representation similarities (default: 0.8)
+ :param var_K: Number of top similarities to consider (default: 20)
+ :param percentile: using to calculate the threshold
+ """
+ import torch
+
+ super().__init__()
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+ self.target_model = target_classifier.model.to(self.device)
+ self.ssl_model = ssl_classifier.model.to(self.device)
+ self.aug_num = aug_num
+ self.alpha = alpha
+ self.var_K = var_K
+
+ self.backbone = self.ssl_model.backbone
+ self.model_classifier = self.ssl_model.classifier
+ self.projector = self.ssl_model.projector
+
+ self.img_augmentations = augmentations
+
+ self.percentile = percentile # determine the threshold
+ self.threshold: float | None = None
+
+ def _multi_transform(self, img: "torch.Tensor") -> "torch.Tensor":
+ import torch
+
+ return torch.stack([self.img_augmentations(img) for _ in range(self.aug_num)], dim=1)
+
+ def _get_metrics(self, x: np.ndarray, batch_size: int = 128) -> np.ndarray:
+ """
+ Calculate similarities that combining label consistency and representation similarity for given samples
+
+ :param x: Input samples
+ :param batch_size: Batch size for processing
+ :return: A report similarities
+ """
+ import torch
+ import torch.nn.functional as F
+
+ samples = torch.from_numpy(x).to(self.device)
+
+ self.target_model.eval()
+ self.backbone.eval()
+ self.model_classifier.eval()
+ self.projector.eval()
+
+ number_batch = int(math.ceil(len(samples) / batch_size))
+
+ similarities_list = []
+
+ with torch.no_grad():
+ for index in range(number_batch):
+ start = index * batch_size
+ end = min((index + 1) * batch_size, len(samples))
+
+ batch_samples = samples[start:end]
+ b, c, h, w = batch_samples.shape
+
+ trans_images = self._multi_transform(batch_samples).to(self.device)
+ ssl_backbone_out = self.backbone(batch_samples)
+
+ ssl_repre = self.projector(ssl_backbone_out)
+ ssl_pred = self.model_classifier(ssl_backbone_out)
+ ssl_label = torch.max(ssl_pred, -1)[1]
+
+ aug_backbone_out = self.backbone(trans_images.reshape(-1, c, h, w))
+ aug_repre = self.projector(aug_backbone_out)
+ aug_pred = self.model_classifier(aug_backbone_out)
+ aug_pred = aug_pred.reshape(b, self.aug_num, -1)
+
+ sim_repre = F.cosine_similarity(
+ ssl_repre.unsqueeze(dim=1), aug_repre.reshape(b, self.aug_num, -1), dim=2
+ )
+
+ sim_preds = F.cosine_similarity(
+ F.one_hot(ssl_label, num_classes=ssl_pred.shape[-1]).unsqueeze(dim=1),
+ aug_pred,
+ dim=2,
+ )
+
+ similarities_list.append(
+ (self.alpha * sim_preds + (1 - self.alpha) * sim_repre).sort(descending=True)[0].cpu().numpy()
+ )
+
+ similarities = np.concatenate(similarities_list, axis=0)
+
+ return similarities
+
+ def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+ """
+ Determine a threshold that covers 95% of clean samples.
+
+ :param x: Clean sample data
+ :param y: Clean sample labels (not used in this method)
+ :param batch_size: Batch size for processing
+ :param nb_epochs: Number of training epochs (not used in this method)
+ """
+ clean_metrics = self._get_metrics(x=x, batch_size=batch_size)
+ k_minus_one_metrics = clean_metrics[:, self.var_K - 1]
+ self.threshold = np.percentile(k_minus_one_metrics, q=self.percentile)
+
+ def detect(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> tuple[np.ndarray, np.ndarray]: # type: ignore
+ """
+ Detect whether given samples are adversarial
+
+ :param x: Input samples
+ :param batch_size: Batch size for processing
+ :return: (report, is_adversarial):
+ where report containing detection results
+ where is_adversarial is a boolean list indicating whether samples are adversarial or not
+ """
+ if self.threshold is None:
+ raise ValueError("Detector has not been fitted. Call fit() before detect().")
+
+ similarities = self._get_metrics(x, batch_size)
+
+ report = similarities[:, self.var_K - 1]
+ is_adversarial = report < self.threshold
+
+ return report, is_adversarial
diff --git a/art/estimators/certification/randomized_smoothing/randomized_smoothing.py b/art/estimators/certification/randomized_smoothing/randomized_smoothing.py
index 3f8a2cd7e3..75f3bbd8ff 100644
--- a/art/estimators/certification/randomized_smoothing/randomized_smoothing.py
+++ b/art/estimators/certification/randomized_smoothing/randomized_smoothing.py
@@ -85,7 +85,7 @@ def predict(self, x: np.ndarray, batch_size: int = 128, verbose: bool = False, *
:type is_abstain: `boolean`
:return: Array of predictions of shape `(nb_inputs, nb_classes)`.
"""
- from scipy.stats import binom_test
+ from scipy.stats import binomtest
is_abstain = kwargs.get("is_abstain")
if is_abstain is not None and not isinstance(is_abstain, bool): # pragma: no cover
@@ -100,12 +100,15 @@ def predict(self, x: np.ndarray, batch_size: int = 128, verbose: bool = False, *
# get class counts
counts_pred = self._prediction_counts(x_i, batch_size=batch_size)
top = counts_pred.argsort()[::-1]
- count1 = np.max(counts_pred)
- count2 = counts_pred[top[1]]
+ # conversion to int
+ count1 = int(np.max(counts_pred))
+ count2 = int(counts_pred[top[1]])
# predict or abstain
smooth_prediction = np.zeros(counts_pred.shape)
- if (not is_abstain) or (binom_test(count1, count1 + count2, p=0.5) <= self.alpha):
+ # get p value from BinomTestResult object
+ p_value = binomtest(count1, count1 + count2, p=0.5).pvalue
+ if (not is_abstain) or (p_value <= self.alpha):
smooth_prediction[np.argmax(counts_pred)] = 1
elif is_abstain:
n_abstained += 1
diff --git a/art/estimators/classification/classifier.py b/art/estimators/classification/classifier.py
index 33e52202ff..33db5bb926 100644
--- a/art/estimators/classification/classifier.py
+++ b/art/estimators/classification/classifier.py
@@ -116,7 +116,7 @@ def nb_classes(self, nb_classes: int):
"""
Set the number of output classes.
"""
- if nb_classes is None or nb_classes < 2:
+ if nb_classes is None or (isinstance(nb_classes, (int, np.integer)) and nb_classes < 2):
raise ValueError("nb_classes must be greater than or equal to 2.")
self._nb_classes = nb_classes
diff --git a/art/estimators/object_detection/pytorch_object_detector.py b/art/estimators/object_detection/pytorch_object_detector.py
index 3d4cc92e29..ff0fbeaa9d 100644
--- a/art/estimators/object_detection/pytorch_object_detector.py
+++ b/art/estimators/object_detection/pytorch_object_detector.py
@@ -66,6 +66,7 @@ def __init__(
"loss_rpn_box_reg",
),
device_type: str = "gpu",
+ is_yolov8: bool = False,
):
"""
Initialization.
@@ -93,6 +94,7 @@ def __init__(
'loss_objectness', and 'loss_rpn_box_reg'.
:param device_type: Type of device to be used for model and tensors, if `cpu` run on CPU, if `gpu` run on GPU
if available otherwise run on CPU.
+ :param is_yolov8: The flag to be used for marking the YOLOv8 model.
"""
import torch
import torchvision
@@ -137,7 +139,11 @@ def __init__(
self._model: torch.nn.Module
self._model.to(self._device)
- self._model.eval()
+ self.is_yolov8 = is_yolov8
+ if self.is_yolov8:
+ self._model.model.eval()
+ else:
+ self._model.eval()
@property
def native_label_is_pytorch_format(self) -> bool:
@@ -403,7 +409,10 @@ def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> list[dict[s
from torch.utils.data import TensorDataset, DataLoader
# Set model to evaluation mode
- self._model.eval()
+ if self.is_yolov8:
+ self._model.model.eval()
+ else:
+ self._model.eval()
# Apply preprocessing and convert to tensors
x_preprocessed, _ = self._preprocess_and_convert_inputs(x=x, y=None, fit=False, no_grad=True)
diff --git a/art/estimators/object_detection/pytorch_yolo.py b/art/estimators/object_detection/pytorch_yolo.py
index cd7fc69b55..cfe251e8bc 100644
--- a/art/estimators/object_detection/pytorch_yolo.py
+++ b/art/estimators/object_detection/pytorch_yolo.py
@@ -64,6 +64,7 @@ def __init__(
"loss_rpn_box_reg",
),
device_type: str = "gpu",
+ is_yolov8: bool = False,
):
"""
Initialization.
@@ -92,6 +93,7 @@ def __init__(
'loss_objectness', and 'loss_rpn_box_reg'.
:param device_type: Type of device to be used for model and tensors, if `cpu` run on CPU, if `gpu` run on GPU
if available otherwise run on CPU.
+ :param is_yolov8: The flag to be used for marking the YOLOv8 model.
"""
super().__init__(
model=model,
@@ -104,6 +106,7 @@ def __init__(
preprocessing=preprocessing,
attack_losses=attack_losses,
device_type=device_type,
+ is_yolov8=is_yolov8,
)
def _translate_labels(self, labels: list[dict[str, "torch.Tensor"]]) -> "torch.Tensor":
diff --git a/art/utils.py b/art/utils.py
index 102720505d..de211a830a 100644
--- a/art/utils.py
+++ b/art/utils.py
@@ -799,15 +799,18 @@ def check_and_transform_label_format(
labels: np.ndarray, nb_classes: int | None, return_one_hot: bool = True
) -> np.ndarray:
"""
- Check label format and transform to one-hot-encoded labels if necessary
+ Check label format and transform to one-hot-encoded labels if necessary. Only supports single-output classification.
:param labels: An array of integer labels of shape `(nb_samples,)`, `(nb_samples, 1)` or `(nb_samples, nb_classes)`.
- :param nb_classes: The number of classes. If None the number of classes is determined automatically.
+ :param nb_classes: The number of classes, as an integer. If None the number of classes is determined automatically.
:param return_one_hot: True if returning one-hot encoded labels, False if returning index labels.
:return: Labels with shape `(nb_samples, nb_classes)` (one-hot) or `(nb_samples,)` (index).
"""
labels_return = labels
+ if nb_classes is not None and not isinstance(nb_classes, (int, np.integer)):
+ raise TypeError("nb_classes that is not an integer is not supported")
+
if len(labels.shape) == 2 and labels.shape[1] > 1: # multi-class, one-hot encoded
if not return_one_hot:
labels_return = np.argmax(labels, axis=1)
diff --git a/docs/modules/attacks/evasion.rst b/docs/modules/attacks/evasion.rst
index f6f41ad95c..39531bcea3 100644
--- a/docs/modules/attacks/evasion.rst
+++ b/docs/modules/attacks/evasion.rst
@@ -50,6 +50,12 @@ Auto Conjugate Gradient (Auto-CG)
:members:
:special-members:
+Rescaling-Auto Conjugate Gradient (ReACG)
+---------------------------------
+.. autoclass:: RescalingAutoConjugateGradient
+ :members:
+ :special-members:
+
Boundary Attack / Decision-Based Attack
---------------------------------------
.. autoclass:: BoundaryAttack
diff --git a/notebooks/snal.ipynb b/notebooks/snal.ipynb
new file mode 100644
index 0000000000..22a663e9cc
--- /dev/null
+++ b/notebooks/snal.ipynb
@@ -0,0 +1,871 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Steal Now and Attack Later\n",
+ "\n",
+ "This notebook provides a demonstration showing how to use ART to launch the SNAL attack [1].\n",
+ "\n",
+ "The core concept of this attack is to first collect objects from any model and then in a second step append valid patches to the target image and weaken the impact of unimportant pixels.\n",
+ "\n",
+ "\n",
+ "[1] Steal Now and Attack Later: Evaluating Robustness of Object Detection against Black-box Adversarial Attacks (https://arxiv.org/abs/2404.15881)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "\n",
+ "logger = logging.getLogger(__name__)\n",
+ "logger.setLevel(level=logging.INFO)\n",
+ "logger.addHandler(logging.StreamHandler(sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#%% Download a target image from MS COCO dataset\n",
+ "from io import BytesIO\n",
+ "from PIL import Image\n",
+ "\n",
+ "import requests\n",
+ "TARGET = 'https://farm2.staticflickr.com/1065/705706084_39a7f28fc9_z.jpg' # val2017/000000552842.jpg\n",
+ "response = requests.get(TARGET)\n",
+ "org_img = np.asarray(Image.open(BytesIO(response.content)).resize((640, 640)))\n",
+ "org_x = np.stack([org_img.transpose((2, 0, 1))], axis=0).astype(np.float32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "#%% Download YOLOv8 model\n",
+ "# If ultralytics is not found, please run the command: `pip install ultralytics`\n",
+ "from ultralytics import YOLO\n",
+ "from art.estimators.object_detection import PyTorchYolo\n",
+ "\n",
+ "model = YOLO('yolov8m')\n",
+ "py_model = PyTorchYolo(model=model,\n",
+ " input_shape=(3, 640, 640),\n",
+ " channels_first=True,\n",
+ " is_yolov8=True)\n",
+ "\n",
+ "# Define a custom function to collect patches from images\n",
+ "def collect_patches_from_images(model: \"torch.nn.Module\",\n",
+ " imgs: \"torch.Tensor\"):\n",
+ " \"\"\"\n",
+ " Collect patches and corrsponding spatial information by the model from images.\n",
+ "\n",
+ " :param model: Object detection model.\n",
+ " :param imgs: Target images.\n",
+ "\n",
+ " :return: Detected objects and corrsponding spatial information.\n",
+ " \"\"\"\n",
+ " import torch\n",
+ "\n",
+ " bs = imgs.shape[0]\n",
+ " with torch.no_grad():\n",
+ " pred = model.model(imgs)\n",
+ " y = []\n",
+ " for obj in pred:\n",
+ " y.append(obj.boxes.xyxy)\n",
+ "\n",
+ " candidates_patch = []\n",
+ " candidates_position = []\n",
+ " for i in range(bs):\n",
+ " patch = []\n",
+ " if y[i].shape[0] == 0:\n",
+ " candidates_patch.append(patch)\n",
+ " candidates_position.append(torch.zeros((0, 4), device=model.device))\n",
+ " continue\n",
+ "\n",
+ " pos_matrix = y[i][:, :4].clone().int()\n",
+ " pos_matrix[:, 0] = torch.clamp_min(pos_matrix[:, 0], 0)\n",
+ " pos_matrix[:, 1] = torch.clamp_min(pos_matrix[:, 1], 0)\n",
+ " pos_matrix[:, 2] = torch.clamp_max(pos_matrix[:, 2], imgs.shape[3])\n",
+ " pos_matrix[:, 3] = torch.clamp_max(pos_matrix[:, 3], imgs.shape[2])\n",
+ " for e in pos_matrix:\n",
+ " p = imgs[i, :, e[1]:e[3], e[0]:e[2]]\n",
+ " patch.append(p.to(model.device))\n",
+ "\n",
+ " candidates_patch.append(patch)\n",
+ " candidates_position.append(pos_matrix)\n",
+ "\n",
+ " return candidates_patch, candidates_position"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#%% Prepare dataset\n",
+ "import os\n",
+ "import time\n",
+ "\n",
+ "# Select images randomly from COCO dataset\n",
+ "list_url = ['http://farm4.staticflickr.com/3572/5744200926_082c11c43c_z.jpg', #000000460229\n",
+ " 'http://farm4.staticflickr.com/3010/2749181045_ed450e5d36_z.jpg', #000000057760\n",
+ " 'http://farm4.staticflickr.com/3826/9451771633_f14cef3a8b_z.jpg', #000000468332\n",
+ " 'http://farm7.staticflickr.com/6194/6106161903_e505cbc192_z.jpg', #000000190841\n",
+ " 'http://farm1.staticflickr.com/48/140268688_947e2bcc96_z.jpg', #000000078420\n",
+ " 'http://farm6.staticflickr.com/5011/5389083366_fdf13f2ee6_z.jpg', #000000309655\n",
+ " 'http://farm4.staticflickr.com/3552/5812461870_eb24c8eac5_z.jpg', #000000293324\n",
+ " 'http://farm4.staticflickr.com/3610/3361019695_1005dd49fd_z.jpg', #000000473821\n",
+ " 'http://farm8.staticflickr.com/7323/9725958435_3359641442_z.jpg', #000000025386\n",
+ " 'http://farm4.staticflickr.com/3317/3427794620_9db24fe462_z.jpg', #000000347693\n",
+ " 'http://farm6.staticflickr.com/5143/5589997131_22f51b308c_z.jpg', #000000058029\n",
+ " 'http://farm5.staticflickr.com/4061/4376326145_7ef66603e3_z.jpg', #000000389933\n",
+ " 'http://farm3.staticflickr.com/2028/2188480725_5fbf27a5b3_z.jpg', #000000311789\n",
+ " 'http://farm1.staticflickr.com/172/421715600_666b0f6a2b_z.jpg', #000000506004\n",
+ " 'http://farm9.staticflickr.com/8331/8100320407_6044d243a5_z.jpg', #000000076648\n",
+ " 'http://farm4.staticflickr.com/3236/2487649513_1ef6a6d5c9_z.jpg', #000000201646\n",
+ " 'http://farm4.staticflickr.com/3094/2684280938_a5b59c0fac_z.jpg', #000000447187\n",
+ " 'http://farm1.staticflickr.com/42/100911501_005e4d3aa8_z.jpg', #000000126107\n",
+ " 'http://farm1.staticflickr.com/56/147795701_40d7bc8331_z.jpg', #000000505942\n",
+ " 'http://farm5.staticflickr.com/4103/5074895283_71a73d77e5_z.jpg', #000000360951\n",
+ " 'http://farm1.staticflickr.com/160/404335548_3bdc1f2ed9_z.jpg', #000000489764\n",
+ " 'http://farm9.staticflickr.com/8446/7857456044_401a257790_z.jpg', #000000407574\n",
+ " ]\n",
+ "\n",
+ "ROOT_MSCOCO = 'datasets'\n",
+ "os.makedirs(ROOT_MSCOCO, exist_ok = True)\n",
+ "for idx, img_url in enumerate(list_url):\n",
+ " response = requests.get(img_url)\n",
+ " with open(f'{ROOT_MSCOCO}/{idx:03d}.jpg', 'wb') as f:\n",
+ " f.write(response.content)\n",
+ " time.sleep(0.5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0: 640x640 1 person, 1 stop sign, 359.4ms\n",
+ "Speed: 0.0ms preprocess, 359.4ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 2\n",
+ "\n",
+ "0: 640x640 12 persons, 295.8ms\n",
+ "Speed: 0.0ms preprocess, 295.8ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 12\n",
+ "\n",
+ "0: 640x640 4 persons, 321.3ms\n",
+ "Speed: 0.0ms preprocess, 321.3ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 4\n",
+ "\n",
+ "0: 640x640 1 person, 1 remote, 279.5ms\n",
+ "Speed: 0.0ms preprocess, 279.5ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 2\n",
+ "\n",
+ "0: 640x640 1 couch, 1 tv, 327.3ms\n",
+ "Speed: 0.0ms preprocess, 327.3ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 2\n",
+ "\n",
+ "0: 640x640 4 persons, 376.3ms\n",
+ "Speed: 0.0ms preprocess, 376.3ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 4\n",
+ "\n",
+ "0: 640x640 2 traffic lights, 326.8ms\n",
+ "Speed: 0.0ms preprocess, 326.8ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 2\n",
+ "\n",
+ "0: 640x640 1 bicycle, 1 car, 3 boats, 291.8ms\n",
+ "Speed: 0.0ms preprocess, 291.8ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 5\n",
+ "\n",
+ "0: 640x640 1 toilet, 386.5ms\n",
+ "Speed: 0.0ms preprocess, 386.5ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 1\n",
+ "\n",
+ "0: 640x640 10 persons, 300.6ms\n",
+ "Speed: 0.0ms preprocess, 300.6ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 10\n",
+ "\n",
+ "0: 640x640 3 bananas, 295.7ms\n",
+ "Speed: 0.0ms preprocess, 295.7ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 3\n",
+ "\n",
+ "0: 640x640 4 persons, 1 boat, 1 fire hydrant, 1 bird, 287.3ms\n",
+ "Speed: 0.0ms preprocess, 287.3ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 7\n",
+ "\n",
+ "0: 640x640 1 person, 1 umbrella, 2 kites, 282.3ms\n",
+ "Speed: 0.0ms preprocess, 282.3ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 4\n",
+ "\n",
+ "0: 640x640 1 bench, 1 bed, 345.9ms\n",
+ "Speed: 0.0ms preprocess, 345.9ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 2\n",
+ "\n",
+ "0: 640x640 1 dog, 318.5ms\n",
+ "Speed: 0.0ms preprocess, 318.5ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 1\n",
+ "\n",
+ "0: 640x640 1 keyboard, 283.9ms\n",
+ "Speed: 0.0ms preprocess, 283.9ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 1\n",
+ "\n",
+ "0: 640x640 (no detections), 307.8ms\n",
+ "Speed: 0.0ms preprocess, 307.8ms inference, 2.3ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 0\n",
+ "\n",
+ "0: 640x640 (no detections), 313.3ms\n",
+ "Speed: 0.0ms preprocess, 313.3ms inference, 3.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 0\n",
+ "\n",
+ "0: 640x640 2 airplanes, 2 trucks, 300.5ms\n",
+ "Speed: 0.0ms preprocess, 300.5ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 4\n",
+ "\n",
+ "0: 640x640 1 boat, 284.9ms\n",
+ "Speed: 0.0ms preprocess, 284.9ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 1\n",
+ "\n",
+ "0: 640x640 1 stop sign, 315.9ms\n",
+ "Speed: 0.0ms preprocess, 315.9ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 1\n",
+ "\n",
+ "0: 640x640 2 persons, 295.9ms\n",
+ "Speed: 0.0ms preprocess, 295.9ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 2\n",
+ "\n",
+ "0: 640x640 1 laptop, 1 mouse, 1 keyboard, 1 cell phone, 326.6ms\n",
+ "Speed: 0.0ms preprocess, 326.6ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "Number of objects are detected: 4\n",
+ "74\n"
+ ]
+ }
+ ],
+ "source": [
+ "#%% Collect patches\n",
+ "import glob\n",
+ "from torchvision import transforms\n",
+ "from torchvision.datasets.vision import VisionDataset\n",
+ "\n",
+ "class CustomDatasetFolder(VisionDataset):\n",
+ " def __init__(self, root, transform=None):\n",
+ " super(CustomDatasetFolder, self).__init__(root)\n",
+ " self.transform = transform\n",
+ " samples = glob.glob(f\"{root}/*.jpg\")\n",
+ "\n",
+ " self.samples = samples\n",
+ "\n",
+ " def __getitem__(self, index):\n",
+ " sample = self._loader(self.samples[index])\n",
+ " if self.transform is not None:\n",
+ " sample = self.transform(sample)\n",
+ " return sample\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.samples)\n",
+ "\n",
+ " def _loader(self, path):\n",
+ " return Image.open(path).convert(\"RGB\")\n",
+ "\n",
+ "img_dataset = CustomDatasetFolder(\n",
+ " ROOT_MSCOCO,\n",
+ " transforms.Compose([\n",
+ " transforms.RandomResizedCrop((640,640)),\n",
+ " transforms.AutoAugment(),\n",
+ " transforms.RandomHorizontalFlip(),\n",
+ " transforms.ToTensor(),\n",
+ " ]))\n",
+ "img_loader = torch.utils.data.DataLoader(img_dataset, batch_size=1, shuffle=True)\n",
+ "\n",
+ "candidates_list = []\n",
+ "TILE_SIZE = 64\n",
+ "MAX_IMGS = 25\n",
+ "img_count = 0\n",
+ "for x in iter(img_loader):\n",
+ " img_count = img_count + 1\n",
+ " if img_count == MAX_IMGS:\n",
+ " break\n",
+ "\n",
+ " candidates, _ = collect_patches_from_images(py_model, x.to(py_model.device))\n",
+ " print(f'Number of objects are detected: {len(candidates[0])}')\n",
+ " candidates_list = candidates_list + candidates[0]\n",
+ "\n",
+ "print(len(candidates_list))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "0: 640x640 3 persons, 2 bicycles, 1 bottle, 312.4ms\n",
+ "1: 640x640 7 persons, 1 bicycle, 312.4ms\n",
+ "2: 640x640 3 persons, 1 bicycle, 312.4ms\n",
+ "3: 640x640 9 persons, 4 bicycles, 312.4ms\n",
+ "4: 640x640 16 persons, 7 bicycles, 312.4ms\n",
+ "5: 640x640 8 persons, 1 bicycle, 1 bottle, 1 sink, 312.4ms\n",
+ "6: 640x640 8 persons, 3 bicycles, 312.4ms\n",
+ "7: 640x640 11 persons, 2 bicycles, 312.4ms\n",
+ "8: 640x640 6 persons, 4 bicycles, 1 bus, 2 sports balls, 1 apple, 312.4ms\n",
+ "9: 640x640 12 persons, 3 bicycles, 1 bus, 312.4ms\n",
+ "Speed: 0.0ms preprocess, 312.4ms inference, 3.1ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 16 persons, 2 airplanes, 1 bus, 1 truck, 328.7ms\n",
+ "1: 640x640 28 persons, 6 airplanes, 1 stop sign, 1 skis, 1 baseball glove, 328.7ms\n",
+ "2: 640x640 14 persons, 2 airplanes, 1 bus, 2 trucks, 1 skis, 328.7ms\n",
+ "3: 640x640 15 persons, 4 airplanes, 1 bus, 1 truck, 3 traffic lights, 2 horses, 1 baseball glove, 328.7ms\n",
+ "4: 640x640 16 persons, 1 airplane, 2 buss, 1 truck, 1 stop sign, 328.7ms\n",
+ "5: 640x640 45 persons, 1 airplane, 2 buss, 1 traffic light, 328.7ms\n",
+ "6: 640x640 20 persons, 1 airplane, 1 truck, 328.7ms\n",
+ "7: 640x640 25 persons, 2 airplanes, 3 buss, 1 stop sign, 1 skis, 328.7ms\n",
+ "8: 640x640 31 persons, 3 airplanes, 2 trucks, 1 stop sign, 328.7ms\n",
+ "9: 640x640 21 persons, 2 airplanes, 1 truck, 1 traffic light, 2 stop signs, 1 baseball bat, 1 baseball glove, 328.7ms\n",
+ "Speed: 0.0ms preprocess, 328.7ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 48 persons, 3 airplanes, 2 buss, 1 sports ball, 1 baseball glove, 2 bowls, 1 keyboard, 313.2ms\n",
+ "1: 640x640 21 persons, 3 airplanes, 3 buss, 1 toilet, 313.2ms\n",
+ "2: 640x640 16 persons, 4 airplanes, 313.2ms\n",
+ "3: 640x640 25 persons, 1 airplane, 1 bus, 1 bowl, 313.2ms\n",
+ "4: 640x640 24 persons, 2 airplanes, 2 buss, 2 toilets, 313.2ms\n",
+ "5: 640x640 24 persons, 313.2ms\n",
+ "6: 640x640 25 persons, 1 car, 3 airplanes, 2 buss, 1 bowl, 1 book, 313.2ms\n",
+ "7: 640x640 26 persons, 1 airplane, 3 buss, 1 bird, 1 dog, 1 skateboard, 3 bowls, 313.2ms\n",
+ "8: 640x640 23 persons, 1 car, 4 buss, 1 traffic light, 1 baseball glove, 1 cell phone, 313.2ms\n",
+ "9: 640x640 15 persons, 1 car, 4 airplanes, 4 buss, 2 tennis rackets, 313.2ms\n",
+ "Speed: 0.0ms preprocess, 313.2ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 2 persons, 1 cat, 322.4ms\n",
+ "1: 640x640 4 persons, 1 orange, 1 carrot, 1 chair, 322.4ms\n",
+ "2: 640x640 1 person, 5 buss, 1 truck, 322.4ms\n",
+ "3: 640x640 2 persons, 1 car, 1 bus, 1 traffic light, 322.4ms\n",
+ "4: 640x640 1 person, 1 bus, 1 baseball bat, 1 bottle, 322.4ms\n",
+ "5: 640x640 9 persons, 1 bus, 322.4ms\n",
+ "6: 640x640 9 persons, 1 bus, 1 keyboard, 322.4ms\n",
+ "7: 640x640 1 person, 1 vase, 322.4ms\n",
+ "8: 640x640 2 persons, 2 buss, 1 chair, 1 keyboard, 322.4ms\n",
+ "9: 640x640 2 persons, 1 bus, 1 chair, 322.4ms\n",
+ "Speed: 0.0ms preprocess, 322.4ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 26 persons, 1 traffic light, 2 skiss, 1 snowboard, 2 bottles, 1 bowl, 328.6ms\n",
+ "1: 640x640 19 persons, 1 traffic light, 1 baseball glove, 328.6ms\n",
+ "2: 640x640 25 persons, 1 suitcase, 1 sports ball, 328.6ms\n",
+ "3: 640x640 18 persons, 1 bus, 328.6ms\n",
+ "4: 640x640 31 persons, 1 traffic light, 1 suitcase, 4 bottles, 328.6ms\n",
+ "5: 640x640 20 persons, 1 bench, 1 tie, 4 suitcases, 1 sports ball, 9 bottles, 328.6ms\n",
+ "6: 640x640 6 persons, 1 baseball glove, 328.6ms\n",
+ "7: 640x640 17 persons, 3 suitcases, 328.6ms\n",
+ "8: 640x640 18 persons, 1 bus, 6 bottles, 328.6ms\n",
+ "9: 640x640 21 persons, 1 baseball bat, 1 bottle, 328.6ms\n",
+ "Speed: 0.0ms preprocess, 328.6ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 10 persons, 3 buss, 1 baseball bat, 1 tv, 1 clock, 312.4ms\n",
+ "1: 640x640 6 persons, 1 car, 1 umbrella, 1 keyboard, 1 teddy bear, 312.4ms\n",
+ "2: 640x640 6 persons, 1 bus, 1 traffic light, 1 sports ball, 2 teddy bears, 312.4ms\n",
+ "3: 640x640 15 persons, 1 traffic light, 1 parking meter, 1 bottle, 1 keyboard, 312.4ms\n",
+ "4: 640x640 6 persons, 1 car, 1 bus, 1 bottle, 2 keyboards, 312.4ms\n",
+ "5: 640x640 13 persons, 2 buss, 1 sports ball, 312.4ms\n",
+ "6: 640x640 15 persons, 2 buss, 1 parking meter, 1 baseball glove, 7 bottles, 1 keyboard, 1 vase, 312.4ms\n",
+ "7: 640x640 14 persons, 1 car, 1 bus, 1 traffic light, 1 parking meter, 1 tv, 2 vases, 312.4ms\n",
+ "8: 640x640 10 persons, 2 cars, 2 buss, 1 baseball glove, 1 keyboard, 312.4ms\n",
+ "9: 640x640 10 persons, 1 car, 2 airplanes, 2 buss, 1 traffic light, 1 tv, 1 vase, 312.4ms\n",
+ "Speed: 0.0ms preprocess, 312.4ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 5 persons, 1 traffic light, 1 surfboard, 3 bottles, 1 mouse, 320.6ms\n",
+ "1: 640x640 3 persons, 1 airplane, 1 traffic light, 320.6ms\n",
+ "2: 640x640 7 persons, 1 traffic light, 1 baseball glove, 1 surfboard, 1 sink, 320.6ms\n",
+ "3: 640x640 7 persons, 1 car, 1 traffic light, 2 teddy bears, 320.6ms\n",
+ "4: 640x640 12 persons, 4 traffic lights, 1 surfboard, 320.6ms\n",
+ "5: 640x640 13 persons, 1 car, 2 airplanes, 4 traffic lights, 1 mouse, 2 teddy bears, 320.6ms\n",
+ "6: 640x640 7 persons, 1 frisbee, 1 baseball glove, 1 surfboard, 3 mouses, 320.6ms\n",
+ "7: 640x640 4 persons, 1 traffic light, 320.6ms\n",
+ "8: 640x640 8 persons, 1 car, 1 bus, 3 traffic lights, 3 bottles, 1 banana, 320.6ms\n",
+ "9: 640x640 8 persons, 5 traffic lights, 1 cow, 1 baseball glove, 1 bottle, 320.6ms\n",
+ "Speed: 0.0ms preprocess, 320.6ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 4 persons, 1 fire hydrant, 357.5ms\n",
+ "1: 640x640 8 persons, 1 banana, 1 remote, 357.5ms\n",
+ "2: 640x640 6 persons, 1 bus, 357.5ms\n",
+ "3: 640x640 12 persons, 1 car, 1 sports ball, 1 baseball bat, 357.5ms\n",
+ "4: 640x640 7 persons, 357.5ms\n",
+ "5: 640x640 13 persons, 1 book, 357.5ms\n",
+ "6: 640x640 10 persons, 2 fire hydrants, 2 baseball bats, 1 banana, 357.5ms\n",
+ "7: 640x640 5 persons, 1 baseball glove, 1 keyboard, 357.5ms\n",
+ "8: 640x640 4 persons, 2 traffic lights, 357.5ms\n",
+ "9: 640x640 8 persons, 357.5ms\n",
+ "Speed: 0.0ms preprocess, 357.5ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 4 persons, 321.8ms\n",
+ "1: 640x640 4 persons, 1 airplane, 1 sports ball, 1 baseball bat, 1 baseball glove, 1 bottle, 1 book, 321.8ms\n",
+ "2: 640x640 5 persons, 321.8ms\n",
+ "3: 640x640 11 persons, 1 baseball glove, 1 refrigerator, 1 teddy bear, 321.8ms\n",
+ "4: 640x640 7 persons, 1 baseball glove, 321.8ms\n",
+ "5: 640x640 8 persons, 1 baseball glove, 1 keyboard, 321.8ms\n",
+ "6: 640x640 4 persons, 1 airplane, 321.8ms\n",
+ "7: 640x640 7 persons, 1 chair, 1 keyboard, 321.8ms\n",
+ "8: 640x640 13 persons, 2 horses, 1 sports ball, 2 bottles, 321.8ms\n",
+ "9: 640x640 5 persons, 1 baseball glove, 321.8ms\n",
+ "Speed: 0.0ms preprocess, 321.8ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 10 persons, 3 buss, 1 truck, 3 bottles, 323.3ms\n",
+ "1: 640x640 23 persons, 7 buss, 2 ties, 2 scissorss, 323.3ms\n",
+ "2: 640x640 8 persons, 5 buss, 1 truck, 1 umbrella, 1 tie, 8 bottles, 323.3ms\n",
+ "3: 640x640 12 persons, 1 bus, 1 umbrella, 1 tie, 1 skis, 1 bottle, 323.3ms\n",
+ "4: 640x640 8 persons, 3 buss, 1 truck, 1 tie, 1 skis, 323.3ms\n",
+ "5: 640x640 23 persons, 1 car, 4 buss, 1 umbrella, 1 tie, 2 sports balls, 323.3ms\n",
+ "6: 640x640 5 persons, 2 buss, 1 bird, 1 horse, 323.3ms\n",
+ "7: 640x640 13 persons, 1 bus, 6 ties, 323.3ms\n",
+ "8: 640x640 18 persons, 9 buss, 1 umbrella, 1 tie, 1 scissors, 323.3ms\n",
+ "9: 640x640 21 persons, 4 buss, 2 ties, 1 baseball glove, 323.3ms\n",
+ "Speed: 0.0ms preprocess, 323.3ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 99 persons, 3 buss, 1 traffic light, 4 bottles, 2 bowls, 351.7ms\n",
+ "Speed: 0.0ms preprocess, 351.7ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 95 persons, 3 buss, 8 snowboards, 1 sports ball, 1 baseball bat, 1 skateboard, 280.4ms\n",
+ "Speed: 0.0ms preprocess, 280.4ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 103 persons, 3 bicycles, 3 buss, 5 traffic lights, 4 bottles, 2 refrigerators, 273.1ms\n",
+ "Speed: 0.0ms preprocess, 273.1ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 72 persons, 1 bus, 4 traffic lights, 1 baseball glove, 1 refrigerator, 273.9ms\n",
+ "Speed: 0.0ms preprocess, 273.9ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 97 persons, 1 bus, 1 train, 1 bowl, 325.0ms\n",
+ "Speed: 0.0ms preprocess, 325.0ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 70 persons, 3 buss, 5 traffic lights, 1 baseball glove, 320.5ms\n",
+ "Speed: 0.0ms preprocess, 320.5ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 56 persons, 2 buss, 1 train, 1 cow, 1 baseball glove, 1 chair, 281.5ms\n",
+ "Speed: 0.0ms preprocess, 281.5ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 58 persons, 1 bus, 1 baseball glove, 1 bowl, 324.0ms\n",
+ "Speed: 0.0ms preprocess, 324.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 41 persons, 2 buss, 2 trains, 1 traffic light, 1 baseball glove, 278.0ms\n",
+ "Speed: 0.0ms preprocess, 278.0ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 37 persons, 4 buss, 1 traffic light, 1 baseball glove, 309.1ms\n",
+ "Speed: 0.0ms preprocess, 309.1ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 25 persons, 1 baseball glove, 1 bowl, 273.9ms\n",
+ "Speed: 0.0ms preprocess, 273.9ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 36 persons, 1 bus, 1 baseball glove, 1 bowl, 295.4ms\n",
+ "Speed: 0.0ms preprocess, 295.4ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 32 persons, 1 bus, 2 trains, 1 baseball glove, 315.9ms\n",
+ "Speed: 0.0ms preprocess, 315.9ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 34 persons, 1 traffic light, 1 baseball glove, 275.3ms\n",
+ "Speed: 0.0ms preprocess, 275.3ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 21 persons, 1 traffic light, 1 baseball glove, 298.4ms\n",
+ "Speed: 0.0ms preprocess, 298.4ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 32 persons, 1 truck, 1 traffic light, 1 baseball glove, 278.6ms\n",
+ "Speed: 0.0ms preprocess, 278.6ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 28 persons, 1 traffic light, 1 baseball glove, 304.6ms\n",
+ "Speed: 0.0ms preprocess, 304.6ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 20 persons, 1 traffic light, 1 baseball glove, 319.6ms\n",
+ "Speed: 0.0ms preprocess, 319.6ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 27 persons, 2 traffic lights, 1 baseball glove, 286.8ms\n",
+ "Speed: 0.0ms preprocess, 286.8ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 25 persons, 2 traffic lights, 1 horse, 1 sports ball, 1 baseball glove, 299.5ms\n",
+ "Speed: 0.0ms preprocess, 299.5ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 23 persons, 3 traffic lights, 1 baseball glove, 276.5ms\n",
+ "Speed: 0.0ms preprocess, 276.5ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 20 persons, 1 traffic light, 1 baseball glove, 279.6ms\n",
+ "Speed: 0.0ms preprocess, 279.6ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 23 persons, 1 traffic light, 1 baseball glove, 330.8ms\n",
+ "Speed: 0.0ms preprocess, 330.8ms inference, 3.2ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 23 persons, 2 trains, 3 traffic lights, 1 baseball glove, 296.2ms\n",
+ "Speed: 0.0ms preprocess, 296.2ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 18 persons, 1 traffic light, 1 elephant, 1 baseball glove, 306.0ms\n",
+ "Speed: 0.0ms preprocess, 306.0ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 25 persons, 1 truck, 2 traffic lights, 1 baseball glove, 275.4ms\n",
+ "Speed: 0.0ms preprocess, 275.4ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 14 persons, 3 traffic lights, 1 baseball glove, 276.6ms\n",
+ "Speed: 0.0ms preprocess, 276.6ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 289.3ms\n",
+ "Speed: 0.0ms preprocess, 289.3ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 17 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 312.9ms\n",
+ "Speed: 0.0ms preprocess, 312.9ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 16 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 345.9ms\n",
+ "Speed: 0.0ms preprocess, 345.9ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 3 traffic lights, 1 sports ball, 1 baseball glove, 284.7ms\n",
+ "Speed: 0.0ms preprocess, 284.7ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 19 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 282.2ms\n",
+ "Speed: 0.0ms preprocess, 282.2ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 276.7ms\n",
+ "Speed: 0.0ms preprocess, 276.7ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 281.3ms\n",
+ "Speed: 0.0ms preprocess, 281.3ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 14 persons, 1 traffic light, 1 sports ball, 1 baseball glove, 313.0ms\n",
+ "Speed: 0.0ms preprocess, 313.0ms inference, 3.1ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 20 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 297.8ms\n",
+ "Speed: 0.0ms preprocess, 297.8ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 11 persons, 1 truck, 1 traffic light, 1 sports ball, 1 baseball glove, 286.6ms\n",
+ "Speed: 0.0ms preprocess, 286.6ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 20 persons, 1 train, 1 traffic light, 1 baseball glove, 279.0ms\n",
+ "Speed: 0.0ms preprocess, 279.0ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 1 traffic light, 1 sports ball, 1 baseball glove, 276.0ms\n",
+ "Speed: 0.0ms preprocess, 276.0ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 22 persons, 1 train, 1 traffic light, 1 sports ball, 1 baseball glove, 293.2ms\n",
+ "Speed: 0.0ms preprocess, 293.2ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 17 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 308.6ms\n",
+ "Speed: 0.0ms preprocess, 308.6ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 14 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 1 chair, 305.6ms\n",
+ "Speed: 0.0ms preprocess, 305.6ms inference, 3.2ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 20 persons, 1 truck, 4 traffic lights, 1 sports ball, 1 baseball glove, 287.1ms\n",
+ "Speed: 0.0ms preprocess, 287.1ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 17 persons, 1 truck, 1 traffic light, 1 sports ball, 1 baseball glove, 284.7ms\n",
+ "Speed: 0.0ms preprocess, 284.7ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 12 persons, 1 truck, 1 traffic light, 1 baseball glove, 446.5ms\n",
+ "Speed: 0.0ms preprocess, 446.5ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 17 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 275.4ms\n",
+ "Speed: 0.0ms preprocess, 275.4ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 17 persons, 3 traffic lights, 1 baseball glove, 334.5ms\n",
+ "Speed: 0.0ms preprocess, 334.5ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 19 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 291.1ms\n",
+ "Speed: 0.0ms preprocess, 291.1ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 19 persons, 1 car, 2 traffic lights, 1 baseball glove, 277.8ms\n",
+ "Speed: 0.0ms preprocess, 277.8ms inference, 3.1ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 24 persons, 1 traffic light, 1 sports ball, 1 baseball glove, 282.8ms\n",
+ "Speed: 0.0ms preprocess, 282.8ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 20 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 284.4ms\n",
+ "Speed: 0.0ms preprocess, 284.4ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 19 persons, 1 truck, 2 traffic lights, 1 sports ball, 1 baseball glove, 293.2ms\n",
+ "Speed: 0.0ms preprocess, 293.2ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 19 persons, 1 traffic light, 1 baseball glove, 325.8ms\n",
+ "Speed: 0.0ms preprocess, 325.8ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 23 persons, 4 traffic lights, 1 sports ball, 1 baseball glove, 359.3ms\n",
+ "Speed: 0.0ms preprocess, 359.3ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 10 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 278.6ms\n",
+ "Speed: 0.0ms preprocess, 278.6ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 281.9ms\n",
+ "Speed: 0.0ms preprocess, 281.9ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 25 persons, 5 traffic lights, 1 sports ball, 1 baseball glove, 322.6ms\n",
+ "Speed: 0.0ms preprocess, 322.6ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 18 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 273.6ms\n",
+ "Speed: 0.0ms preprocess, 273.6ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 11 persons, 2 traffic lights, 1 baseball glove, 337.3ms\n",
+ "Speed: 0.0ms preprocess, 337.3ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 17 persons, 1 traffic light, 1 sports ball, 1 baseball glove, 314.1ms\n",
+ "Speed: 0.0ms preprocess, 314.1ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 3 traffic lights, 1 sports ball, 1 baseball glove, 462.5ms\n",
+ "Speed: 0.0ms preprocess, 462.5ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 20 persons, 1 truck, 1 traffic light, 1 baseball glove, 279.0ms\n",
+ "Speed: 0.0ms preprocess, 279.0ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 1 traffic light, 1 sports ball, 1 baseball glove, 281.4ms\n",
+ "Speed: 0.0ms preprocess, 281.4ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 273.4ms\n",
+ "Speed: 0.0ms preprocess, 273.4ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 22 persons, 1 traffic light, 1 sports ball, 1 baseball glove, 284.6ms\n",
+ "Speed: 0.0ms preprocess, 284.6ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 18 persons, 1 truck, 2 traffic lights, 1 sports ball, 1 baseball glove, 305.3ms\n",
+ "Speed: 0.0ms preprocess, 305.3ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 16 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 279.3ms\n",
+ "Speed: 0.0ms preprocess, 279.3ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 22 persons, 2 traffic lights, 1 sports ball, 1 baseball glove, 298.1ms\n",
+ "Speed: 0.0ms preprocess, 298.1ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 11 persons, 1 train, 1 traffic light, 1 sports ball, 1 baseball glove, 392.5ms\n",
+ "Speed: 0.0ms preprocess, 392.5ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 14 persons, 1 train, 1 traffic light, 1 sports ball, 1 baseball glove, 282.5ms\n",
+ "Speed: 0.0ms preprocess, 282.5ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 13 persons, 1 bus, 1 truck, 1 boat, 1 traffic light, 1 sports ball, 1 baseball glove, 274.9ms\n",
+ "Speed: 0.0ms preprocess, 274.9ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 19 persons, 1 train, 2 traffic lights, 1 sports ball, 1 baseball glove, 297.7ms\n",
+ "Speed: 0.0ms preprocess, 297.7ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 13 persons, 1 train, 1 sports ball, 1 baseball glove, 292.6ms\n",
+ "Speed: 0.0ms preprocess, 292.6ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 16 persons, 1 train, 1 sports ball, 1 baseball glove, 303.9ms\n",
+ "Speed: 0.0ms preprocess, 303.9ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 11 persons, 1 train, 1 sports ball, 1 baseball glove, 275.1ms\n",
+ "Speed: 0.0ms preprocess, 275.1ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 12 persons, 2 trains, 1 sports ball, 1 baseball glove, 277.5ms\n",
+ "Speed: 0.0ms preprocess, 277.5ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 13 persons, 1 train, 1 baseball glove, 321.6ms\n",
+ "Speed: 0.0ms preprocess, 321.6ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 8 persons, 1 train, 1 truck, 1 sports ball, 1 baseball glove, 378.1ms\n",
+ "Speed: 0.0ms preprocess, 378.1ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 10 persons, 1 train, 1 elephant, 1 baseball glove, 291.5ms\n",
+ "Speed: 0.0ms preprocess, 291.5ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 17 persons, 1 train, 1 sports ball, 1 baseball glove, 351.4ms\n",
+ "Speed: 0.0ms preprocess, 351.4ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 13 persons, 1 train, 1 boat, 1 sports ball, 1 baseball glove, 295.6ms\n",
+ "Speed: 0.0ms preprocess, 295.6ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 11 persons, 1 train, 1 baseball glove, 272.8ms\n",
+ "Speed: 0.0ms preprocess, 272.8ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 13 persons, 2 trains, 1 sports ball, 1 baseball glove, 280.0ms\n",
+ "Speed: 0.0ms preprocess, 280.0ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 9 persons, 1 train, 1 sports ball, 1 baseball glove, 313.3ms\n",
+ "Speed: 0.0ms preprocess, 313.3ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 11 persons, 1 train, 1 baseball glove, 298.9ms\n",
+ "Speed: 0.0ms preprocess, 298.9ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 10 persons, 1 train, 1 sports ball, 1 baseball glove, 280.6ms\n",
+ "Speed: 0.0ms preprocess, 280.6ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 1 train, 1 horse, 1 sports ball, 1 baseball glove, 277.6ms\n",
+ "Speed: 0.0ms preprocess, 277.6ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 14 persons, 3 trains, 1 sports ball, 1 baseball glove, 276.9ms\n",
+ "Speed: 0.0ms preprocess, 276.9ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 14 persons, 1 train, 1 baseball glove, 326.7ms\n",
+ "Speed: 0.0ms preprocess, 326.7ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 22 persons, 1 train, 1 sports ball, 1 baseball glove, 309.7ms\n",
+ "Speed: 0.0ms preprocess, 309.7ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 9 persons, 1 train, 1 sports ball, 1 baseball glove, 297.3ms\n",
+ "Speed: 0.0ms preprocess, 297.3ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 11 persons, 1 train, 1 boat, 1 sports ball, 1 baseball glove, 286.2ms\n",
+ "Speed: 0.0ms preprocess, 286.2ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 18 persons, 1 train, 1 horse, 1 sports ball, 1 baseball glove, 281.2ms\n",
+ "Speed: 0.0ms preprocess, 281.2ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 16 persons, 1 train, 1 sports ball, 1 baseball glove, 293.2ms\n",
+ "Speed: 0.0ms preprocess, 293.2ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 12 persons, 1 train, 1 sports ball, 1 baseball glove, 305.1ms\n",
+ "Speed: 0.0ms preprocess, 305.1ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 1 train, 1 truck, 1 sports ball, 1 baseball glove, 274.7ms\n",
+ "Speed: 0.0ms preprocess, 274.7ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 14 persons, 1 train, 1 truck, 1 sports ball, 1 baseball glove, 277.9ms\n",
+ "Speed: 0.0ms preprocess, 277.9ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 15 persons, 1 train, 1 truck, 1 sports ball, 1 baseball glove, 275.6ms\n",
+ "Speed: 0.0ms preprocess, 275.6ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 10 persons, 1 train, 1 sports ball, 1 baseball glove, 323.3ms\n",
+ "Speed: 0.0ms preprocess, 323.3ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 17 persons, 1 train, 1 sports ball, 1 baseball glove, 274.7ms\n",
+ "Speed: 0.0ms preprocess, 274.7ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 12 persons, 1 train, 1 elephant, 1 sports ball, 1 baseball glove, 312.2ms\n",
+ "Speed: 0.0ms preprocess, 312.2ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 134 persons, 3 buss, 1 traffic light, 3 snowboards, 1 baseball glove, 8 bottles, 3 bowls, 1 chair, 292.0ms\n",
+ "Speed: 0.0ms preprocess, 292.0ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 640)\n",
+ "\n",
+ "0: 640x640 1 person, 1 sports ball, 1 baseball glove, 286.7ms\n",
+ "1: 640x640 105 persons, 1 bus, 1 traffic light, 1 snowboard, 1 baseball glove, 286.7ms\n",
+ "Speed: 4.9ms preprocess, 286.7ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n"
+ ]
+ }
+ ],
+ "source": [
+ "#%% Apply attack\n",
+ "from art.attacks.evasion import SNAL\n",
+ "attack = SNAL(py_model,\n",
+ " eps = 16.0 /255.0,\n",
+ " max_iter = 100,\n",
+ " num_grid = 10,\n",
+ " candidates=candidates_list,\n",
+ " collector=collect_patches_from_images)\n",
+ "x_adv = (attack.generate(org_x / 255.0) * 255.0)\n",
+ "adv_np = np.transpose(x_adv[0, :], (1, 2, 0)).astype(np.uint8)\n",
+ "Image.fromarray(adv_np).save(f'output.png')\n",
+ "Image.fromarray(org_img).save(f'target.png')\n",
+ "\n",
+ "# Visualize the results\n",
+ "from IPython.display import Image as PyImage\n",
+ "results = model(['target.png', 'output.png'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "3\n"
+ ]
+ },
+ {
+ "data": {
+ "image/jpeg": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "results[0].save(\"results.jpg\")\n",
+ "logger.info(len(results[0].boxes.xyxy))\n",
+ "PyImage(filename=f\"results.jpg\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "109\n"
+ ]
+ },
+ {
+ "data": {
+ "image/jpeg": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "results[1].save(\"results.jpg\")\n",
+ "logger.info(len(results[1].boxes.xyxy))\n",
+ "PyImage(filename=f\"results.jpg\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirements_test.txt b/requirements_test.txt
index a623075fe8..be4223444d 100644
--- a/requirements_test.txt
+++ b/requirements_test.txt
@@ -6,7 +6,7 @@ matplotlib==3.7.1
scikit-learn==1.4.1.post1
six==1.16.0
Pillow==10.3.0
-tqdm==4.66.4
+tqdm==4.67.1
statsmodels==0.14.2
pydub==0.25.1
resampy==0.4.3
@@ -64,7 +64,7 @@ types-PyYAML==6.0.12.20240917
types-setuptools==71.1.0.20240726
# other
-requests~=2.31.0
+requests~=2.32.3
ultralytics==8.0.217
ipython==8.25.0
diff --git a/run_tests.sh b/run_tests.sh
index 71ae377a42..81d1cea461 100755
--- a/run_tests.sh
+++ b/run_tests.sh
@@ -146,6 +146,10 @@ else
"tests/defences/test_rounded.py" \
"tests/defences/test_thermometer_encoding.py" \
"tests/defences/test_variance_minimization.py" \
+ "tests/defences/detector/evasion/test_beyond_detector.py" \
+ "tests/defences/detector/evasion/test_binary_activation_detector.py" \
+ "tests/defences/detector/evasion/test_binary_input_detector.py" \
+ "tests/defences/detector/evasion/test_subsetscanning_detector.py" \
"tests/defences/detector/poison/test_activation_defence.py" \
"tests/defences/detector/poison/test_clustering_analyzer.py" \
"tests/defences/detector/poison/test_ground_truth_evaluator.py" \
diff --git a/tests/attacks/evasion/test_auto_attack.py b/tests/attacks/evasion/test_auto_attack.py
index 52e76274a6..f98847bfd0 100644
--- a/tests/attacks/evasion/test_auto_attack.py
+++ b/tests/attacks/evasion/test_auto_attack.py
@@ -273,7 +273,7 @@ def test_generate_parallel(art_warning, fix_get_mnist_subset, image_dl_estimator
batch_size=batch_size,
estimator_orig=None,
targeted=False,
- parallel=True,
+ parallel_pool_size=3,
)
attack_noparallel = AutoAttack(
@@ -285,7 +285,7 @@ def test_generate_parallel(art_warning, fix_get_mnist_subset, image_dl_estimator
batch_size=batch_size,
estimator_orig=None,
targeted=False,
- parallel=False,
+ parallel_pool_size=0,
)
x_train_mnist_adv = attack.generate(x=x_train_mnist, y=y_train_mnist)
@@ -310,7 +310,7 @@ def test_generate_parallel(art_warning, fix_get_mnist_subset, image_dl_estimator
batch_size=batch_size,
estimator_orig=None,
targeted=True,
- parallel=True,
+ parallel_pool_size=3,
)
x_train_mnist_adv = attack.generate(x=x_train_mnist, y=y_train_mnist)
diff --git a/tests/attacks/evasion/test_rescaling_auto_conjugate_gradient.py b/tests/attacks/evasion/test_rescaling_auto_conjugate_gradient.py
new file mode 100644
index 0000000000..9bb9026b1c
--- /dev/null
+++ b/tests/attacks/evasion/test_rescaling_auto_conjugate_gradient.py
@@ -0,0 +1,166 @@
+# MIT License
+
+# Copyright (c) 2024 Keiichiro Yamamura
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2024
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import pytest
+
+import numpy as np
+
+from art.attacks.evasion import RescalingAutoConjugateGradient
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+ (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+ n_train = 100
+ n_test = 10
+ yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.parametrize("loss_type", ["cross_entropy", "difference_logits_ratio"])
+@pytest.mark.parametrize("norm", ["inf", np.inf, 1, 2])
+@pytest.mark.skip_framework("keras", "non_dl_frameworks", "mxnet", "kerastf", "tensorflow1", "tensorflow2v1")
+def test_generate(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack, framework, loss_type, norm):
+ print("test_generate")
+ try:
+ classifier = image_dl_estimator_for_attack(RescalingAutoConjugateGradient, from_logits=True)
+
+ print("framework", framework)
+
+ if framework in ["tensorflow1", "tensorflow2v1"] and loss_type == "difference_logits_ratio":
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(
+ estimator=classifier,
+ norm=norm,
+ eps=0.3,
+ eps_step=0.1,
+ max_iter=5,
+ targeted=False,
+ nb_random_init=1,
+ batch_size=32,
+ loss_type=loss_type,
+ verbose=False,
+ )
+ else:
+
+ attack = RescalingAutoConjugateGradient(
+ estimator=classifier,
+ norm=norm,
+ eps=0.3,
+ eps_step=0.1,
+ max_iter=5,
+ targeted=False,
+ nb_random_init=1,
+ batch_size=32,
+ loss_type=loss_type,
+ verbose=False,
+ )
+
+ (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+
+ x_train_mnist_adv = attack.generate(x=x_train_mnist, y=y_train_mnist)
+
+ assert np.max(np.abs(x_train_mnist_adv - x_train_mnist)) > 0.0
+
+ except ARTTestException as e:
+ art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_check_params(art_warning, image_dl_estimator_for_attack):
+ try:
+
+ classifier = image_dl_estimator_for_attack(RescalingAutoConjugateGradient, from_logits=True)
+
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, norm=0)
+
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, eps="1")
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, eps=-1.0)
+
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, eps_step="1")
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, eps_step=-1.0)
+
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, max_iter=1.0)
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, max_iter=-1)
+
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, targeted="true")
+
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, nb_random_init=1.0)
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, nb_random_init=-1)
+
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, batch_size=1.0)
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, batch_size=-1)
+
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, loss_type="test")
+
+ with pytest.raises(ValueError):
+ _ = RescalingAutoConjugateGradient(classifier, verbose="true")
+
+ except ARTTestException as e:
+ art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_classifier_type_check_fail(art_warning):
+ try:
+ backend_test_classifier_type_check_fail(
+ RescalingAutoConjugateGradient, [BaseEstimator, LossGradientsMixin, ClassifierMixin]
+ )
+ except ARTTestException as e:
+ art_warning(e)
diff --git a/tests/attacks/evasion/test_steal_now_attack_later.py b/tests/attacks/evasion/test_steal_now_attack_later.py
new file mode 100644
index 0000000000..ddbba0b3d9
--- /dev/null
+++ b/tests/attacks/evasion/test_steal_now_attack_later.py
@@ -0,0 +1,241 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2024
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+
+import numpy as np
+import pytest
+
+from art.attacks.evasion import SNAL
+from art.estimators.object_detection import PyTorchYolo
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_generate(art_warning):
+ try:
+ # The ultralytics package does not support Python versions earlier than 3.8.
+ # To avoid an import error with the TF 1.x pipeline, it is imported only within the function scope.
+ import torch
+ import requests
+ from ultralytics import YOLO
+
+ model = YOLO("yolov8m")
+ py_model = PyTorchYolo(model=model, input_shape=(3, 640, 640), channels_first=True, is_yolov8=True)
+
+ # Define a custom function to collect patches from images
+ def collect_patches_from_images(model, imgs):
+ bs = imgs.shape[0]
+ with torch.no_grad():
+ pred = model.model(imgs)
+ y = []
+ for obj in pred:
+ y.append(obj.boxes.xyxy)
+
+ candidates_patch = []
+ candidates_position = []
+ for i in range(bs):
+ patch = []
+ if y[i].shape[0] == 0:
+ candidates_patch.append(patch)
+ candidates_position.append(torch.zeros((0, 4), device=model.device))
+ continue
+
+ pos_matrix = y[i][:, :4].clone().int()
+ pos_matrix[:, 0] = torch.clamp_min(pos_matrix[:, 0], 0)
+ pos_matrix[:, 1] = torch.clamp_min(pos_matrix[:, 1], 0)
+ pos_matrix[:, 2] = torch.clamp_max(pos_matrix[:, 2], imgs.shape[3])
+ pos_matrix[:, 3] = torch.clamp_max(pos_matrix[:, 3], imgs.shape[2])
+ for e in pos_matrix:
+ p = imgs[i, :, e[1] : e[3], e[0] : e[2]]
+ patch.append(p.to(model.device))
+
+ candidates_patch.append(patch)
+ candidates_position.append(pos_matrix)
+
+ return candidates_patch, candidates_position
+
+ # Download a sample image
+ from io import BytesIO
+ from PIL import Image
+
+ TARGET = "https://farm2.staticflickr.com/1065/705706084_39a7f28fc9_z.jpg" # val2017/000000552842.jpg
+ response = requests.get(TARGET)
+ org_img = np.asarray(Image.open(BytesIO(response.content)).resize((640, 640)))
+ x_org = np.stack([org_img.transpose((2, 0, 1))], axis=0).astype(np.float32)
+
+ # Prepare dataset
+ import os
+ import time
+
+ # Select images randomly from COCO dataset
+ list_url = [
+ "http://farm4.staticflickr.com/3572/5744200926_082c11c43c_z.jpg", # 000000460229
+ "http://farm4.staticflickr.com/3010/2749181045_ed450e5d36_z.jpg", # 000000057760
+ "http://farm4.staticflickr.com/3826/9451771633_f14cef3a8b_z.jpg", # 000000468332
+ "http://farm7.staticflickr.com/6194/6106161903_e505cbc192_z.jpg", # 000000190841
+ "http://farm1.staticflickr.com/48/140268688_947e2bcc96_z.jpg", # 000000078420
+ "http://farm6.staticflickr.com/5011/5389083366_fdf13f2ee6_z.jpg", # 000000309655
+ "http://farm4.staticflickr.com/3552/5812461870_eb24c8eac5_z.jpg", # 000000293324
+ "http://farm4.staticflickr.com/3610/3361019695_1005dd49fd_z.jpg", # 000000473821
+ "http://farm8.staticflickr.com/7323/9725958435_3359641442_z.jpg", # 000000025386
+ "http://farm4.staticflickr.com/3317/3427794620_9db24fe462_z.jpg", # 000000347693
+ "http://farm6.staticflickr.com/5143/5589997131_22f51b308c_z.jpg", # 000000058029
+ "http://farm5.staticflickr.com/4061/4376326145_7ef66603e3_z.jpg", # 000000389933
+ "http://farm3.staticflickr.com/2028/2188480725_5fbf27a5b3_z.jpg", # 000000311789
+ "http://farm1.staticflickr.com/172/421715600_666b0f6a2b_z.jpg", # 000000506004
+ "http://farm9.staticflickr.com/8331/8100320407_6044d243a5_z.jpg", # 000000076648
+ "http://farm4.staticflickr.com/3236/2487649513_1ef6a6d5c9_z.jpg", # 000000201646
+ "http://farm4.staticflickr.com/3094/2684280938_a5b59c0fac_z.jpg", # 000000447187
+ "http://farm1.staticflickr.com/42/100911501_005e4d3aa8_z.jpg", # 000000126107
+ "http://farm1.staticflickr.com/56/147795701_40d7bc8331_z.jpg", # 000000505942
+ "http://farm5.staticflickr.com/4103/5074895283_71a73d77e5_z.jpg", # 000000360951
+ "http://farm1.staticflickr.com/160/404335548_3bdc1f2ed9_z.jpg", # 000000489764
+ "http://farm9.staticflickr.com/8446/7857456044_401a257790_z.jpg", # 000000407574
+ ]
+
+ ROOT_MSCOCO = "datasets"
+ os.makedirs(ROOT_MSCOCO, exist_ok=True)
+ for idx, img_url in enumerate(list_url):
+ response = requests.get(img_url)
+ with open(f"{ROOT_MSCOCO}/{idx:03d}.jpg", "wb") as f:
+ f.write(response.content)
+ time.sleep(0.5)
+
+ # % Collect patches
+ import glob
+ from torchvision import transforms
+ from torchvision.datasets.vision import VisionDataset
+
+ class CustomDatasetFolder(VisionDataset):
+ def __init__(self, root, transform=None):
+ super(CustomDatasetFolder, self).__init__(root)
+ self.transform = transform
+ samples = glob.glob(f"{root}/*.jpg")
+
+ self.samples = samples
+
+ def __getitem__(self, index):
+ sample = self._loader(self.samples[index])
+ if self.transform is not None:
+ sample = self.transform(sample)
+ return sample
+
+ def __len__(self):
+ return len(self.samples)
+
+ def _loader(self, path):
+ return Image.open(path).convert("RGB")
+
+ img_dataset = CustomDatasetFolder(
+ ROOT_MSCOCO,
+ transforms.Compose(
+ [
+ transforms.RandomResizedCrop((640, 640)),
+ transforms.AutoAugment(),
+ transforms.RandomHorizontalFlip(),
+ transforms.ToTensor(),
+ ]
+ ),
+ )
+ img_loader = torch.utils.data.DataLoader(img_dataset, batch_size=1, shuffle=True)
+
+ candidates_list = []
+ MAX_IMGS = 25
+ img_count = 0
+ for x in iter(img_loader):
+ img_count = img_count + 1
+ if img_count == MAX_IMGS:
+ break
+
+ candidates, _ = collect_patches_from_images(py_model, x.to(py_model.device))
+ print(f"Number of objects are detected: {len(candidates[0])}")
+ candidates_list = candidates_list + candidates[0]
+
+ attack = SNAL(
+ py_model,
+ eps=16.0 / 255.0,
+ max_iter=100,
+ num_grid=10,
+ candidates=candidates_list,
+ collector=collect_patches_from_images,
+ )
+
+ x_adv = attack.generate(x_org / 255.0)
+ assert x_org.shape == x_adv.shape
+ assert np.min(x_adv) >= 0.0
+ assert np.max(x_adv) <= 1.0
+
+ adv_np = np.transpose(x_adv[0, :] * 255, (1, 2, 0)).astype(np.uint8)
+ result = model(adv_np)
+ assert len(result[0].boxes.xyxy) > 25
+
+ except ARTTestException as e:
+ art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_check_params(art_warning):
+ try:
+ # The ultralytics package does not support Python versions earlier than 3.8.
+ # To avoid an import error with the TF 1.x pipeline, it is imported only within the function scope.
+ from ultralytics import YOLO
+
+ model = YOLO("yolov8m")
+ py_model = PyTorchYolo(model=model, input_shape=(3, 640, 640), channels_first=True, is_yolov8=True)
+
+ def dummy_func(model, imags):
+ candidates_patch = []
+ candidates_position = []
+ return candidates_patch, candidates_position
+
+ dummy_list = [[], []]
+
+ with pytest.raises(ValueError):
+ _ = SNAL(estimator=py_model, eps=-1.0, max_iter=5, num_grid=10, candidates=dummy_list, collector=dummy_func)
+ with pytest.raises(ValueError):
+ _ = SNAL(estimator=py_model, eps=2.0, max_iter=5, num_grid=10, candidates=dummy_list, collector=dummy_func)
+ with pytest.raises(TypeError):
+ _ = SNAL(
+ estimator=py_model,
+ eps=8 / 255.0,
+ max_iter=1.0,
+ num_grid=10,
+ candidates=dummy_list,
+ collector=dummy_func,
+ )
+ with pytest.raises(ValueError):
+ _ = SNAL(
+ estimator=py_model, eps=8 / 255.0, max_iter=0, num_grid=10, candidates=dummy_list, collector=dummy_func
+ )
+ with pytest.raises(TypeError):
+ _ = SNAL(
+ estimator=py_model, eps=8 / 255.0, max_iter=5, num_grid=1.0, candidates=dummy_list, collector=dummy_func
+ )
+ with pytest.raises(ValueError):
+ _ = SNAL(
+ estimator=py_model, eps=8 / 255.0, max_iter=5, num_grid=0, candidates=dummy_list, collector=dummy_func
+ )
+ with pytest.raises(TypeError):
+ _ = SNAL(estimator=py_model, eps=8 / 255.0, max_iter=5, num_grid=10, candidates=1.0, collector=dummy_func)
+ with pytest.raises(ValueError):
+ _ = SNAL(estimator=py_model, eps=8 / 255.0, max_iter=5, num_grid=10, candidates=[], collector=dummy_func)
+
+ except ARTTestException as e:
+ art_warning(e)
diff --git a/tests/defences/detector/evasion/test_beyond_detector.py b/tests/defences/detector/evasion/test_beyond_detector.py
new file mode 100644
index 0000000000..ba5cdc2871
--- /dev/null
+++ b/tests/defences/detector/evasion/test_beyond_detector.py
@@ -0,0 +1,178 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2024
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import pytest
+import numpy as np
+
+from art.attacks.evasion.fast_gradient import FastGradientMethod
+from art.defences.detector.evasion import BeyondDetectorPyTorch
+from art.estimators.classification import PyTorchClassifier
+from tests.utils import ARTTestException
+
+
+def get_ssl_model(weights_path):
+ """
+ Loads the SSL model (SimSiamWithCls).
+ """
+ import torch
+ import torch.nn as nn
+
+ class SimSiamWithCls(nn.Module):
+ """
+ SimSiam with Classifier
+ """
+
+ def __init__(self, arch="resnet18", feat_dim=2048, num_proj_layers=2):
+ from torchvision import models
+
+ super(SimSiamWithCls, self).__init__()
+ self.backbone = models.resnet18()
+ out_dim = self.backbone.fc.weight.shape[1]
+ self.backbone.conv1 = nn.Conv2d(
+ in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=2, bias=False
+ )
+ self.backbone.maxpool = nn.Identity()
+ self.backbone.fc = nn.Identity()
+ self.classifier = nn.Linear(out_dim, out_features=10)
+
+ pred_hidden_dim = int(feat_dim / 4)
+
+ self.projector = nn.Sequential(
+ nn.Linear(out_dim, feat_dim, bias=False),
+ nn.BatchNorm1d(feat_dim),
+ nn.ReLU(),
+ nn.Linear(feat_dim, feat_dim, bias=False),
+ nn.BatchNorm1d(feat_dim),
+ nn.ReLU(),
+ nn.Linear(feat_dim, feat_dim),
+ nn.BatchNorm1d(feat_dim, affine=False),
+ )
+ self.projector[6].bias.requires_grad = False
+
+ self.predictor = nn.Sequential(
+ nn.Linear(feat_dim, pred_hidden_dim, bias=False),
+ nn.BatchNorm1d(pred_hidden_dim),
+ nn.ReLU(),
+ nn.Linear(pred_hidden_dim, feat_dim),
+ )
+
+ def forward(self, img, im_aug1=None, im_aug2=None):
+
+ r_ori = self.backbone(img)
+ if im_aug1 is None and im_aug2 is None:
+ cls = self.classifier(r_ori)
+ rep = self.projector(r_ori)
+ return {"cls": cls, "rep": rep}
+ else:
+
+ r1 = self.backbone(im_aug1)
+ r2 = self.backbone(im_aug2)
+
+ z1 = self.projector(r1)
+ z2 = self.projector(r2)
+
+ p1 = self.predictor(z1)
+ p2 = self.predictor(z2)
+
+ return {"z1": z1, "z2": z2, "p1": p1, "p2": p2}
+
+ model = SimSiamWithCls()
+ model.load_state_dict(torch.load(weights_path))
+ return model
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_beyond_detector(art_warning, get_default_cifar10_subset):
+ try:
+ import torch
+ from torchvision import models, transforms
+
+ # Load CIFAR10 data
+ (x_train, y_train), (x_test, _) = get_default_cifar10_subset
+
+ x_train = x_train[0:100]
+ y_train = y_train[0:100]
+ x_test = x_test[0:100]
+
+ # Load models
+ # Download pretrained weights from
+ # https://drive.google.com/drive/folders/1ieEdd7hOj2CIl1FQfu4-3RGZmEj-mesi?usp=sharing
+ target_model = models.resnet18()
+ # target_model.load_state_dict(torch.load("./utils/resources/models/resnet_c10.pth", map_location=torch.device('cpu')))
+ ssl_model = get_ssl_model(weights_path="./utils/resources/models/simsiam_c10.pth")
+
+ target_classifier = PyTorchClassifier(
+ model=target_model, nb_classes=10, input_shape=(3, 32, 32), loss=torch.nn.CrossEntropyLoss()
+ )
+ ssl_classifier = PyTorchClassifier(
+ model=ssl_model, nb_classes=10, input_shape=(3, 32, 32), loss=torch.nn.CrossEntropyLoss()
+ )
+
+ # Generate adversarial samples
+ attack = FastGradientMethod(estimator=target_classifier, eps=0.05)
+ x_test_adv = attack.generate(x_test)
+
+ img_augmentations = transforms.Compose(
+ [
+ transforms.RandomResizedCrop(32, scale=(0.2, 1.0)),
+ transforms.RandomHorizontalFlip(),
+ transforms.RandomApply([transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8), # not strengthened
+ transforms.RandomGrayscale(p=0.2),
+ transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+ ]
+ )
+
+ # Initialize BeyondDetector
+ detector = BeyondDetectorPyTorch(
+ target_classifier=target_classifier,
+ ssl_classifier=ssl_classifier,
+ augmentations=img_augmentations,
+ aug_num=50,
+ alpha=0.8,
+ var_K=20,
+ percentile=5,
+ )
+
+ # Fit the detector
+ detector.fit(x_train, y_train, batch_size=128)
+
+ # Apply detector on clean and adversarial test data
+ _, test_detection = detector.detect(x_test)
+ _, test_adv_detection = detector.detect(x_test_adv)
+
+ # Assert there is at least one true positive and negative
+ nb_true_positives = np.sum(test_adv_detection)
+ nb_true_negatives = len(test_detection) - np.sum(test_detection)
+
+ assert nb_true_positives > 0
+ assert nb_true_negatives > 0
+
+ clean_accuracy = 1 - np.mean(test_detection)
+ adv_accuracy = np.mean(test_adv_detection)
+
+ assert clean_accuracy > 0.0
+ assert adv_accuracy > 0.0
+
+ except ARTTestException as e:
+ art_warning(e)
+
+
+if __name__ == "__main__":
+
+ test_beyond_detector()
diff --git a/tests/estimators/classification/test_scikitlearn.py b/tests/estimators/classification/test_scikitlearn.py
index 56ecf30e57..7fbcfe87b4 100644
--- a/tests/estimators/classification/test_scikitlearn.py
+++ b/tests/estimators/classification/test_scikitlearn.py
@@ -47,6 +47,7 @@
ScikitlearnSVC,
)
from art.estimators.classification.scikitlearn import SklearnClassifier
+from art.utils import check_and_transform_label_format
from tests.utils import TestBase, master_seed
@@ -80,6 +81,28 @@ def test_save(self):
def test_clone_for_refitting(self):
_ = self.classifier.clone_for_refitting()
+ def test_multi_label(self):
+ x_train = self.x_train_iris
+ y_train = self.y_train_iris
+ x_test = self.x_test_iris
+ y_test = self.y_test_iris
+
+ # make multi-label binary
+ y_train = np.column_stack((y_train, y_train, y_train))
+ y_train[y_train > 1] = 1
+ y_test = np.column_stack((y_test, y_test, y_test))
+ y_test[y_test > 1] = 1
+
+ underlying_model = DecisionTreeClassifier()
+ underlying_model.fit(x_train, y_train)
+ model = ScikitlearnDecisionTreeClassifier(model=underlying_model)
+
+ pred = model.predict(x_test)
+ assert pred[0].shape[0] == x_test.shape[0]
+ assert isinstance(model.nb_classes, np.ndarray)
+ with self.assertRaises(TypeError):
+ check_and_transform_label_format(y_train, nb_classes=model.nb_classes)
+
class TestScikitlearnExtraTreeClassifier(TestBase):
@classmethod
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