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cifar10_DistillationNet_Cons-Def_blackdefense (copy).py
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"""
This tutorial shows how to generate transferable attacks from adversarial examples of the source model
The target model is CNN that is structured in 4 convolutional layers, 2 pooling layers, and 3 fully connected layers.
The target model requires the input with the size of 32*32, therefore, the source examples are resized for implementation.
Xintao Ding
School of Computer and Information, Anhui Normal University
"""
# pylint: disable=missing-docstring
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import tensorflow as tf
from cleverhans.attacks import FastGradientMethod
from cleverhans.augmentation import random_horizontal_flip, random_shift
from cleverhans.compat import flags
from cleverhans.dataset import CIFAR10
from cleverhans.loss import CrossEntropy
#from cleverhans.model_zoo.all_convolutional import ModelAllConvolutional
from cleverhans.model_zoo.four_conv2FC import Model4Convolutional2FC#Revised by Ding
from cleverhans.utils_tf import model_eval
from cleverhans.data_extenv2 import data_exten#Added by Ding
#from cleverhans.data_exten_mulpro import data_exten
from sklearn.metrics import roc_curve, roc_auc_score#Added by Ding
import matplotlib.pyplot as plt
from PIL import Image
import cv2
import multiprocessing
FLAGS = flags.FLAGS
BATCH_SIZE = 128
LEARNING_RATE = 0.001
BACKPROP_THROUGH_ATTACK = False
NB_FILTERS = 64
def cifar10_tutorial(train_start=0, train_end=50000, test_start=0,
test_end=10000, batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE,
testing=False,
backprop_through_attack=BACKPROP_THROUGH_ATTACK,
nb_filters=NB_FILTERS, num_threads=None,
label_smoothing=0.1):
"""
CIFAR10 cleverhans tutorial
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param learning_rate: learning rate for training
:param clean_train: perform normal training on clean examples only
before performing adversarial training.
:param testing: if true, complete an AccuracyReport for unit tests
to verify that performance is adequate
:param backprop_through_attack: If True, backprop through adversarial
example construction process during
adversarial training.
:param label_smoothing: float, amount of label smoothing for cross entropy
:return: an AccuracyReport object
"""
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Create TF session
if num_threads:
config_args = dict(intra_op_parallelism_threads=1)
else:
config_args = {}
sess = tf.Session(config=tf.ConfigProto(**config_args))
# Get CIFAR10 data
data = CIFAR10(train_start=train_start, train_end=train_end,
test_start=test_start, test_end=test_end)
dataset_size = data.x_train.shape[0]
dataset_train = data.to_tensorflow()[0]
dataset_train = dataset_train.map(
lambda x, y: (random_shift(random_horizontal_flip(x)), y), 4)
dataset_train = dataset_train.batch(batch_size)
dataset_train = dataset_train.prefetch(16)
x_train, y_train = data.get_set('train')
x_test, y_test = data.get_set('test')
# plt.imshow(np.uint8(x_test[0,:,:,:]*255))
# Use Image Parameters
img_rows, img_cols, nchannels = x_test.shape[1:4]
nb_classes = y_test.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
eval_params = {'batch_size': batch_size}
net_height=32
net_width=32
# model = ModelAllConvolutional('model1', nb_classes, nb_filters,
# input_shape=[32, 32, 3])
model = Model4Convolutional2FC('model1', nb_classes, nb_filters, input_shape=[net_height, net_width, nchannels])
preds = model.get_logits(x)
probs = model.get_probs(x)
#########################################Added by Ding
saver = tf.train.Saver()
saver.restore(sess,'./models/cifar10/cifar10_dtcnn_train_epoch89')#target model
print("aaaaaaaaaaaaaaaaa=+++++++++++++++++++++++++++++")
#########################################
# np.save("cifar10_DistillationNet_augmodel_fgsmlinf_10000adv",adv)#save advs produced on clean model:
adv = np.load("cifar10_vgg16_augmodel_pgd_10000adv.npy")#source examples for attack
x_tt =x_train[:100,:,:,:]
y_tt = y_train[:100,:]
dat0ext=data_exten(x_tt, y_tt, 100, base_range=4)
print("advaaaaaaaaaaaaaaaaaaaaaaaaaaa:{},{}".format(np.sum(adv-x_test),adv.shape))
# Evaluate the accuracy of the model on benign and adversarial examples
accuracy,suc_att_exam = model_eval(sess, x, y, preds, x_test, y_test, args=eval_params)
print('Test accuracy on legitimate test examples: {}'.format(accuracy))
adv_accuracy,adv_suc_att_exam = model_eval(sess, x, y, preds, adv, y_test, args=eval_params)
print('Test accuracy on adversarial test examples: {}'.format(adv_accuracy))
print('Test Attack Successful Rate (ASR) on examples: {0:.4f}' .format (1-adv_accuracy))
#for untargeted attack, suc_att_exam[i] is true means a successful classified examples
#for targeted attack, suc_att_exam[i] is true means a successful attack, it counts succeful attacked examples
percent_perturbed = np.mean(np.sum((adv - x_test)**2, axis=(1, 2, 3))**.5)
dsae=0
kk=0
for i in range(len(adv_suc_att_exam)):
if adv_suc_att_exam[i]==0 and suc_att_exam[i]>0:#adversarial is misclassified but its corresponding binign example is correctly detected
dsae+=np.sum((adv[i,:,:,:] - x_test[i,:,:,:])**2)**.5
kk += 1
dsae=dsae/kk
print("For untargeted attack, the number of misclassified examples (successful attack), sum(adv_suc_att_exam==0):{}, dsae:{}".format(sum(adv_suc_att_exam==0),dsae))
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
print('The number of successful attack:{}, Avg. L_2 norm of perturbations on successful attack / dsae:{}'.format(kk,dsae))
pad_size=4
x_test=np.pad(x_test,((0,0),(pad_size,pad_size),(pad_size,pad_size),(0,0)),'reflect')
x_testcrop = np.zeros((len(x_test),net_height,net_width,3),dtype=np.float32)
adv = np.round(adv*256)/256.0
adv = np.pad(adv,((0,0),(pad_size,pad_size),(pad_size,pad_size),(0,0)),'reflect')
advcrop = np.zeros((len(adv),net_height,net_width,3),dtype=np.float32)
for i in range(len(adv)):
tf_image = adv[i,:,:,:]
test_image = x_test[i,:,:,:]
lu1 = np.random.randint(0,pad_size*2)
lu2 = np.random.randint(0,pad_size*2)
advcrop[i,:,:,:] = tf_image[lu1:lu1+net_height,lu2:lu2+net_width,:]
x_testcrop[i,:,:,:] = test_image[lu1:lu1+net_height,lu2:lu2+net_width,:]
adv = advcrop
x_test = x_testcrop
batch_size = 500 #
base_range=4
n_pert = base_range**nchannels
ext_bat = n_pert+1
logits_ext = np.zeros((test_end*n_pert,nb_classes),dtype=np.float32)
logits_adv_ext = np.zeros((test_end*n_pert,nb_classes),dtype=np.float32)
test_prob_pertpart=np.zeros((test_end*n_pert,nb_classes),dtype=np.float32)
adv_prob_pertpart=np.zeros((test_end*n_pert,nb_classes),dtype=np.float32)
y_test_pertpart = np.zeros((test_end*n_pert,nb_classes),dtype=np.float32)
y_adv_pertpart = np.zeros((test_end*n_pert,nb_classes),dtype=np.float32)
x_adv_pertpart = np.zeros((batch_size*n_pert*2,net_height,net_width,nchannels),dtype=np.float32)
x_test_pertpart = np.zeros((batch_size*n_pert*2,net_height,net_width,nchannels),dtype=np.float32)
val_max_steps = int(len(adv) / batch_size/2)
adv_prob_legit = np.zeros((test_end,nb_classes),dtype=np.float32)
test_prob_legit = np.zeros((test_end,nb_classes),dtype=np.float32)
# Close TF session
sess.close()
def main(argv=None):
from cleverhans_tutorials import check_installation
check_installation(__file__)
cifar10_tutorial(batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
backprop_through_attack=FLAGS.backprop_through_attack,
nb_filters=FLAGS.nb_filters)
if __name__ == '__main__':
flags.DEFINE_integer('nb_filters', NB_FILTERS,
'Model size multiplier')
flags.DEFINE_integer('batch_size', BATCH_SIZE,
'Size of training batches')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
flags.DEFINE_bool('backprop_through_attack', BACKPROP_THROUGH_ATTACK,
('If True, backprop through adversarial example '
'construction process during adversarial training'))
tf.app.run()