cifar10_vgg16_Cons-Def_fgsm_defense.py

"""
This tutorial shows how to implement Cons-Def against FGSM white-box attacks.
Xintao Ding
School of Computer and Information, Anhui Normal University
xintaoding@163.com
"""

import numpy as np
import tensorflow as tf

import slim.nets.vgg160 as vgg
from cifar10_create_tf_record import get_example_nums, read_records, get_batch_images
import tensorflow.contrib.slim as slim

from cleverhans.compat import reduce_max, reduce_sum, softmax_cross_entropy_with_logits
from cleverhans import utils_tf

from cleverhans.data_exten_mulpro import data_exten
from sklearn.metrics import roc_curve, roc_auc_score
#import copy
import multiprocessing

labels_nums = 10  #  the number of labels
batch_size = 20  
resize_height = 32  # Cifar10 size
resize_width = 32   
net_height= 160#the size must be suitable with layer in vgg (net, 4096, [5, 5], padding=fc_conv_padding, scope='fc6'), 224*224 corresponds to [7, 7], 160*160 correpsonds to [5, 5],
net_width = 160#vgg16 size
depths = 3
data_shape = [batch_size, resize_height, resize_width, depths]

input_images = tf.placeholder(dtype=tf.float32, shape=[None, net_height, net_width, depths], name='input')
input_labels = tf.placeholder(dtype=tf.int32, shape=[None, labels_nums], name='label')
y = tf.placeholder(dtype=tf.int32, shape=[None, labels_nums])
is_training = tf.placeholder(tf.bool, name='is_training')

val_record_file='cifar10_extensions/cifar10_test.tfrecords_seg'
val_nums=get_example_nums(val_record_file)
print('val nums:%d'%(val_nums))

val_images, val_labels = read_records([val_record_file], resize_height, resize_width, depths, type='normalization') 
val_images_batch, val_labels_batch = get_batch_images(val_images, val_labels,
                                                              batch_size=batch_size, labels_nums=labels_nums,
                                                              one_hot=True, shuffle=False, num_threads=1)
val_images_batch = tf.image.resize_images(val_images_batch,size=(net_height, net_width))
val_images_batch = tf.rint(val_images_batch*256.)*(1. / 256)

with slim.arg_scope(vgg.vgg_arg_scope()):
    out, end_points = vgg.vgg_16(inputs=input_images, num_classes=labels_nums, is_training=is_training)
probs = tf.nn.softmax(out)

tf.losses.softmax_cross_entropy(onehot_labels=input_labels, logits=out)
loss = tf.losses.get_total_loss(add_regularization_losses=True)
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(input_labels, 1)), tf.float32))

#########################################Added by Ding  
saver = tf.train.Saver()
eps = 0.03
clip_min = 0.
clip_max = 1.
ord=np.inf

def fgm(x,
        logits,
        y=None,
        eps=0.3,
        ord=np.inf,
        loss_fn=softmax_cross_entropy_with_logits,
        clip_min=None,
        clip_max=None,
        clip_grad=False,
        targeted=False,
        sanity_checks=True):
  """
  TensorFlow implementation of the Fast Gradient Method.
  :param x: the input placeholder
  :param logits: output of model.get_logits
  :param y: (optional) A placeholder for the true labels. If targeted
            is true, then provide the target label. Otherwise, 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.
            Labels should be one-hot-encoded.
  :param eps: the epsilon (input variation parameter)
  :param ord: (optional) Order of the norm (mimics NumPy).
              Possible values: np.inf, 1 or 2.
  :param loss_fn: Loss function that takes (labels, logits) as arguments and returns loss
  :param clip_min: Minimum float value for adversarial example components
  :param clip_max: Maximum float value for adversarial example components
  :param clip_grad: (optional bool) Ignore gradient components
                    at positions where the input is already at the boundary
                    of the domain, and the update step will get clipped out.
  :param targeted: Is the attack targeted or untargeted? Untargeted, the
                   default, will try to make the label incorrect. Targeted
                   will instead try to move in the direction of being more
                   like y.
  :return: a tensor for the adversarial example
  """

  asserts = []

  print("OOOOOOOOOOOOOOOOOOOOOOOO:{}".format(ord))

  # If a data range was specified, check that the input was in that range
  if clip_min is not None:
    asserts.append(utils_tf.assert_greater_equal(
        x, tf.cast(clip_min, x.dtype)))

  if clip_max is not None:
    asserts.append(utils_tf.assert_less_equal(x, tf.cast(clip_max, x.dtype)))

  # Make sure the caller has not passed probs by accident
  assert logits.op.type != 'Softmax'

  if y is None:
    # Using model predictions as ground truth to avoid label leaking
    preds_max = reduce_max(logits, 1, keepdims=True)
    y = tf.to_float(tf.equal(logits, preds_max))
    y = tf.stop_gradient(y)
  y = y / reduce_sum(y, 1, keepdims=True)
  
  print("yyyyyyyyyyyyyyyyyyyyyy={}".format(y))

  # Compute loss
  loss = loss_fn(labels=y, logits=logits)
  if targeted:
    loss = -loss

  # Define gradient of loss wrt input
  grad, = tf.gradients(loss, x)

  if clip_grad:
    grad = utils_tf.zero_out_clipped_grads(grad, x, clip_min, clip_max)

  optimal_perturbation = optimize_linear(grad, eps, ord)

  # Add perturbation to original example to obtain adversarial example
  adv_x = x + optimal_perturbation
  
  print("adv_x=============================:{}".format((adv_x)))
#  assert 1==2

  # If clipping is needed, reset all values outside of [clip_min, clip_max]
  if (clip_min is not None) or (clip_max is not None):
    # We don't currently support one-sided clipping
    assert clip_min is not None and clip_max is not None
    adv_x = utils_tf.clip_by_value(adv_x, clip_min, clip_max)
    print("optimal_perturbation:{},adv_x:{}, clip_min:{}, clip_max:{}".format(optimal_perturbation,adv_x, clip_min, clip_max))

  if sanity_checks:
    with tf.control_dependencies(asserts):
      adv_x = tf.identity(adv_x)

  return adv_x, grad, loss, y, optimal_perturbation

def optimize_linear(grad, eps, ord=np.inf):
  """
  Solves for the optimal input to a linear function under a norm constraint.

  Optimal_perturbation = argmax_{eta, ||eta||_{ord} < eps} dot(eta, grad)

  :param grad: tf tensor containing a batch of gradients
  :param eps: float scalar specifying size of constraint region
  :param ord: int specifying order of norm
  :returns:
    tf tensor containing optimal perturbation
  """

  # In Python 2, the `list` call in the following line is redundant / harmless.
  # In Python 3, the `list` call is needed to convert the iterator returned by `range` into a list.
  red_ind = list(range(1, len(grad.get_shape())))
  avoid_zero_div = 1e-12
  if ord == np.inf:
    # Take sign of gradient
    optimal_perturbation = tf.sign(grad)
    # The following line should not change the numerical results.
    # It applies only because `optimal_perturbation` is the output of
    # a `sign` op, which has zero derivative anyway.
    # It should not be applied for the other norms, where the
    # perturbation has a non-zero derivative.
    optimal_perturbation = tf.stop_gradient(optimal_perturbation)
  elif ord == 1:
    abs_grad = tf.abs(grad)
    sign = tf.sign(grad)
    max_abs_grad = tf.reduce_max(abs_grad, red_ind, keepdims=True)
    tied_for_max = tf.to_float(tf.equal(abs_grad, max_abs_grad))
    num_ties = tf.reduce_sum(tied_for_max, red_ind, keepdims=True)
    optimal_perturbation = sign * tied_for_max / num_ties
  elif ord == 2:
    square = tf.maximum(avoid_zero_div,
                        reduce_sum(tf.square(grad),
                                   reduction_indices=red_ind,
                                   keepdims=True))
    optimal_perturbation = grad / tf.sqrt(square)
  else:
    raise NotImplementedError("Only L-inf, L1 and L2 norms are "
                              "currently implemented.")

  # Scale perturbation to be the solution for the norm=eps rather than
  # norm=1 problem
  scaled_perturbation = utils_tf.mul(eps, optimal_perturbation)
  return scaled_perturbation  

adv_x, grad_x, loss_x, _, _  = fgm(input_images, logits=out, eps=eps, clip_min=clip_min, clip_max=clip_max)

val_max_steps = int(val_nums/batch_size)
n_lev=255

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  sess.run(tf.local_variables_initializer())
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(sess=sess, coord=coord)
  
  saver.restore(sess,'models/cifar10/vgg16_models_860000+1783000_0.8809.ckpt')
  print("aaaaaaaaaaaaaaaaa=+++++++++++++++++++++++++++++")
#########################################

  x_test = np.zeros((val_nums,net_height,net_width,depths),dtype=np.float32)
  y_test = np.zeros((val_nums,labels_nums),dtype=np.float32)
  logits = np.zeros((val_nums,labels_nums),dtype=np.float32)
  logits_adv = np.zeros((val_nums,labels_nums),dtype=np.float32)
  adv = np.zeros((val_nums,net_height,net_width,depths),dtype=np.float32)
  
  for i in range(val_max_steps):
      if i%10==0:
          print("i:{}".format(i))
      val_x_bat, val_y_bat = sess.run([val_images_batch, val_labels_batch])

      feed_dict = {input_images: val_x_bat, is_training:False}
      logits_bat = sess.run(out, feed_dict=feed_dict)
  
      feed_dict = {input_images: val_x_bat, is_training:False}
      adv_bat = sess.run(adv_x, feed_dict=feed_dict)

      feed_dict = {input_images: adv_bat, is_training:False}
      logits_adv_bat = sess.run(out, feed_dict=feed_dict)

      x_test[i*batch_size:(i+1)*batch_size,:,:,:] = val_x_bat
      y_test[i*batch_size:(i+1)*batch_size,:] = val_y_bat
      adv[i*batch_size:(i+1)*batch_size,:,:,:] = adv_bat
      logits[i*batch_size:(i+1)*batch_size,:] = logits_bat
      logits_adv[i*batch_size:(i+1)*batch_size,:] = logits_adv_bat
#########################################
  coord.request_stop()
  coord.join(threads)
  np.save("cifar10_vgg16_augmodel_fgsminf_10000adv", adv)
  
 #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
#  dsae_f=0
  adv_suc_att_exam = np.equal(np.argmax(logits_adv,axis=1),np.argmax(y_test,axis=1))
  suc_att_exam = np.equal(np.argmax(logits,axis=1),np.argmax(y_test,axis=1))
  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))
  
  logits = np.argmax(logits,axis=1)
  logits_adv = np.argmax(logits_adv,axis=1)
  y_test_argmax = np.argmax(y_test,axis=1)
  acc = np.sum(np.equal(logits,y_test_argmax))/len(y_test_argmax)
  acc_adv = np.sum(np.equal(logits_adv,y_test_argmax))/len(y_test_argmax)
  print('Test accuracy on legitimate test examples: %0.4f' % (acc))
  print('Test accuracy on adversarial test examples: %0.4f' % (acc_adv))
  
  pad_size=16
  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 = 10
  test_end = val_nums
  base_range=4
  n_pert = base_range**depths
  ext_bat = n_pert+1

  logits_ext = np.zeros((val_nums*n_pert,labels_nums),dtype=np.float32)
  logits_adv_ext = np.zeros((val_nums*n_pert,labels_nums),dtype=np.float32)
  test_prob_pertpart = np.zeros((val_nums*n_pert,labels_nums),dtype=np.float32)
  adv_prob_pertpart = np.zeros((val_nums*n_pert,labels_nums),dtype=np.float32)
  y_test_pertpart = np.zeros((val_nums*n_pert,labels_nums),dtype=np.float32)
  y_adv_pertpart = np.zeros((val_nums*n_pert,labels_nums),dtype=np.float32)
  x_adv_pertpart = np.zeros((batch_size*n_pert*2,net_height,net_width,depths),dtype=np.float32)
  x_test_pertpart = np.zeros((batch_size*n_pert*2,net_height,net_width,depths),dtype=np.float32)
  val_max_steps = int(len(adv)/batch_size/2)
  
  adv_prob_legit = np.zeros((val_nums,labels_nums),dtype=np.float32)
  test_prob_legit = np.zeros((val_nums,labels_nums),dtype=np.float32)
  
  manager=multiprocessing.Manager()
  
  for i in range(val_max_steps):
      rt_res_adv1=manager.dict()
      rt_res_adv2=manager.dict()
      rt_res_test1=manager.dict()
      rt_res_test2=manager.dict()
      p1 = multiprocessing.Process(target=data_exten,args=(adv[i*2*batch_size:(2*i+1)*batch_size,:,:,:],
                                                           y_test[i*2*batch_size:(2*i+1)*batch_size,:],
                                          batch_size, base_range,labels_nums,net_height,net_width,3,
                                          rt_res_adv1))
      p2 = multiprocessing.Process(target=data_exten,args=(adv[(2*i+1)*batch_size:2*(i+1)*batch_size,:,:,:],
                                                           y_test[(2*i+1)*batch_size:2*(i+1)*batch_size,:],
                                          batch_size, base_range,labels_nums,net_height,net_width,3,
                                          rt_res_adv2))
      p3 = multiprocessing.Process(target=data_exten,args=(x_test[i*2*batch_size:(2*i+1)*batch_size,:,:,:],
                                                           y_test[i*2*batch_size:(2*i+1)*batch_size,:],
                                          batch_size, base_range,labels_nums,net_height,net_width,3,
                                          rt_res_test1))
      p4 = multiprocessing.Process(target=data_exten,args=(x_test[(2*i+1)*batch_size:2*(i+1)*batch_size,:,:,:],
                                                           y_test[(2*i+1)*batch_size:2*(i+1)*batch_size,:],
                                          batch_size, base_range,labels_nums,net_height,net_width,3,
                                          rt_res_test2))
      p1.start()
      p2.start()
      p3.start()
      p4.start()
      p1.join()
      x_adv_extended1, y_adv_extended1 = rt_res_adv1.values()
      p2.join()
      x_adv_extended2, y_adv_extended2 = rt_res_adv2.values()
      p3.join()
      x_test_extended1, y_test_extended1 = rt_res_test1.values()
      p4.join()
      x_test_extended2, y_test_extended2 = rt_res_test2.values()
      
      x_adv_pertpart[:batch_size*n_pert,:,:,:] = x_adv_extended1[:batch_size*n_pert,:,:,:]
      x_adv_pertpart[batch_size*n_pert:2*batch_size*n_pert,:,:,:] = x_adv_extended2[:batch_size*n_pert,:,:,:]
      y_adv_pertpart[i*2*batch_size*n_pert:(2*i+1)*batch_size*n_pert,:] = y_adv_extended1[:batch_size*n_pert,:]
      y_adv_pertpart[(2*i+1)*batch_size*n_pert:2*(i+1)*batch_size*n_pert,:] = y_adv_extended2[:batch_size*n_pert,:]

      x_test_pertpart[:batch_size*n_pert,:,:,:] = x_test_extended1[:batch_size*n_pert,:,:,:]
      x_test_pertpart[batch_size*n_pert:2*batch_size*n_pert,:,:,:] = x_test_extended2[:batch_size*n_pert,:,:,:]
      y_test_pertpart[i*2*batch_size*n_pert:(2*i+1)*batch_size*n_pert,:] = y_test_extended1[:batch_size*n_pert,:]
      y_test_pertpart[(2*i+1)*batch_size*n_pert:2*(i+1)*batch_size*n_pert,:] = y_test_extended2[:batch_size*n_pert,:]

      feed_dict = {input_images: adv[2*i*batch_size:2*(i+1)*batch_size,:,:,:], is_training: False}
      adv_prob_legit[2*i*batch_size:2*(i+1)*batch_size,:] = sess.run(probs, feed_dict = feed_dict)
      feed_dict = {input_images: x_test[2*i*batch_size:2*(i+1)*batch_size,:,:,:], is_training: False}
      test_prob_legit[2*i*batch_size:2*(i+1)*batch_size,:] = sess.run(probs, feed_dict = feed_dict)
      
      l_bat=len(x_adv_pertpart)
      jsteps = int(l_bat/batch_size)
      for j in range(jsteps):
          val_x_bat = x_test_pertpart[j*batch_size:(j+1)*batch_size,:,:,:]
          val_adv_bat = x_adv_pertpart[j*batch_size:(j+1)*batch_size,:,:,:]
          feed_dict = {input_images: val_x_bat, is_training: False}
          logits_bat = sess.run(out, feed_dict=feed_dict)
          
          feed_dict = {input_images: val_adv_bat, is_training: False}
          logits_adv_bat = sess.run(out, feed_dict=feed_dict)
          
          y_test_prob = sess.run(probs, feed_dict={input_images: val_x_bat, is_training: False})
          y_adv_prob = sess.run(probs, feed_dict={input_images: val_adv_bat, is_training: False})
          
          logits_ext[2*i*batch_size*n_pert+j*batch_size:2*i*batch_size*n_pert+(j+1)*batch_size,:]=logits_bat
          logits_adv_ext[2*i*batch_size*n_pert+j*batch_size:2*i*batch_size*n_pert+(j+1)*batch_size,:]=logits_adv_bat
          test_prob_pertpart[2*i*batch_size*n_pert+j*batch_size:2*i*batch_size*n_pert+(j+1)*batch_size,:]=y_test_prob
          adv_prob_pertpart[2*i*batch_size*n_pert+j*batch_size:2*i*batch_size*n_pert+(j+1)*batch_size,:]=y_adv_prob
#########################################

  auc_score_test = roc_auc_score(y_test, test_prob_legit)
  auc_score_adv = roc_auc_score(y_test, adv_prob_legit)
  print('auc_score_test:{}, auc_score_adv:{}'.format(auc_score_test, auc_score_adv))
  auc_score_test_ext = roc_auc_score(y_test_pertpart, test_prob_pertpart)
  auc_score_adv_ext = roc_auc_score(y_test_pertpart, adv_prob_pertpart)
  print('auc on extended examples, auc_score_test_ext:{}, auc_score_adv_ext:{}'.format(auc_score_test_ext, auc_score_adv_ext))

  y_test_ext = np.argmax(y_test_pertpart, axis=1)
  cur_preds = np.argmax(logits_ext, axis=1)
  cur_preds_adv = np.argmax(logits_adv_ext, axis=1)
  y_test_ext = np.reshape(y_test_ext,(len(y_test_pertpart)//n_pert,n_pert))
  logits_ext = np.reshape(cur_preds,(len(cur_preds)//n_pert,n_pert))
  logits_adv_ext = np.reshape(cur_preds_adv,(len(cur_preds_adv)//n_pert,n_pert))
  acc_ext = np.sum(np.equal(logits_ext,y_test_ext))/y_test_ext.shape[0]/y_test_ext.shape[1]
  acc_adv_ext = np.sum(np.equal(logits_adv_ext,y_test_ext))/y_test_ext.shape[0]/y_test_ext.shape[1]
  print('Test accuracy on legitimate examples extened by x_test: %0.4f' % (acc_ext))
  print('Test accuracy on extended examples of adversarials: %0.4f' % (acc_adv_ext))
  
  test_result_stat = np.zeros((ext_bat,),dtype=np.float32)
  adv_result_stat = np.zeros((ext_bat,),dtype=np.float32)

  eva_thresh = np.linspace(32,64,9).astype('int32')#from 32 to 64 with a length 9
  len_thresh = len(eva_thresh)
  distrib_incons_preds = np.zeros((len_thresh,n_pert),dtype=np.int32)
  distrib_incons_preds_adv = np.zeros((len_thresh,n_pert),dtype=np.int32)
  
  auc_div_mat = np.zeros((len(cur_preds),n_pert+1),dtype=np.int32)
  auc_div_mat_adv = np.zeros((len(cur_preds),n_pert+1),dtype=np.int32)
  

  for i in range(len(y_test_ext)):
      temp = np.sum(np.equal(logits_ext[i,:],y_test_ext[i,:]))
      auc_div_mat[i,temp] = 1
      test_result_stat[temp] = test_result_stat[temp]+1
      a = np.unique(logits_ext[i,:])
      for j in range(len_thresh):
        if temp<eva_thresh[j]:
          kk = []
          for k in range(len(a)):
              kk.extend([np.sum(logits_ext[i,:]==a[k])])
          ind = np.max(np.array(kk))
          distrib_incons_preds[j,ind-1] = distrib_incons_preds[j,ind-1]+1

  for i in range(len(y_test_ext)):
      temp = np.sum(np.equal(logits_adv_ext[i,:],y_test_ext[i,:]))
      auc_div_mat_adv[i,temp] = 1
      adv_result_stat[temp] = adv_result_stat[temp]+1
      a = np.unique(logits_adv_ext[i,:])
      for j in range(len_thresh):
        if temp<eva_thresh[j]:
          kk = []
          for k in range(len(a)):
              kk.extend([np.sum(logits_adv_ext[i,:]==a[k])])
          ind = np.max(np.array(kk))
          distrib_incons_preds_adv[j,ind-1] = distrib_incons_preds_adv[j,ind-1]+1
#For a benign, thare are n_pert extension images.
#And there are n_pert classifications of the extension of a benign. They may be different or same
#The maximum occurrence of the classification labels is called consistent rank.
#e.g., n_pert=5, and the classification labels of a benign are (0, 2, 2, 1, 2), then the consistent rank of the benign is 3 that is the occurrence of the label 2.
#Furthermore, correct consistent rank is the number of the extensions of a benign that are correctly classified
#test_result_stat[i]=k
#i: correct consistent rank, i=0, 1, 2, ..., n_pert-1
#k is the count of the correct consistent rank i on test images
  print("test_result_stat:{},{}".format(np.sum(test_result_stat),test_result_stat))
  print("adv_result_stat:{},{}".format(np.sum(adv_result_stat),adv_result_stat))
  for i in range(len_thresh):
#distrib_incons_preds3 is the count of consistent rank on the test images with correct consistent rank less than 3
    print("test_result cannot be classified stat (Threshold {}):{},{}".format(eva_thresh[i],np.sum(distrib_incons_preds[i,:]),distrib_incons_preds[i,:]))
#distrib_incons_preds4 is the count of consistent rank on the test images with correct consistent rank less than 4
#    print("test_result cannot be classified stat (Threshold 4):{},{}".format(np.sum(distrib_incons_preds4),distrib_incons_preds4))
#classfication: a benign with N(consistent rank)>=3 is labeled consistent rank
#The number of correctly classified benign is N(correct consistent rank)>=3
    print("The number of benigns that are correctly classified (Threshold {}):{}".format(eva_thresh[i],np.sum(test_result_stat[eva_thresh[i]-len(adv_result_stat):])))
#The number of incorrectly classified benign is the cardinality of the set {example | N(consistent rank)>=3,  true-label(example)~=consistent rank}
    print("The number of benigns that are misclassified (Threshold {}):{}".format(eva_thresh[i],np.sum(distrib_incons_preds[i,eva_thresh[i]-len(adv_result_stat):])))
    print("The number of benigns that are incorrectly detected as adv (Threshold {}):{}".format(eva_thresh[i],np.sum(distrib_incons_preds[i,:eva_thresh[i]-1])))
  
    print("adv_result cannot be classifed stat (Threshold {}):{},{}".format(eva_thresh[i],np.sum(distrib_incons_preds_adv[i,:]),distrib_incons_preds_adv[i,:]))
    print("The number of adv that are correctly classified (Threshold {}):{}".format(eva_thresh[i],np.sum(adv_result_stat[eva_thresh[i]-len(adv_result_stat):])))
    print("The number of adv that are misclassified (Threshold {}):{}".format(eva_thresh[i],np.sum(distrib_incons_preds_adv[i,eva_thresh[i]-len(adv_result_stat):])))
    print("The number of adv that are correctly detected as adv (Threshold {}):{}".format(eva_thresh[i],np.sum(distrib_incons_preds_adv[i,:eva_thresh[i]-1])))

  ####calculate auc
    benign_ind_clc = np.argwhere(np.sum(auc_div_mat[:,eva_thresh[i]:],axis=1)==1)[:,0]
    adv_ind_clc = np.argwhere(np.sum(auc_div_mat_adv[:,eva_thresh[i]:],axis=1)==1)[:,0]
    benign_inds = benign_ind_clc*n_pert
    adv_inds = adv_ind_clc*n_pert
    for j in range(1,n_pert):
        benign_inds = np.concatenate((benign_inds,benign_ind_clc*n_pert+j), axis=0)
        adv_inds = np.concatenate((adv_inds,adv_ind_clc*n_pert+j), axis=0)
    ground_labels = y_test_pertpart[tuple(benign_inds),:]
    del_ind = []
    for j in range(labels_nums):
        if np.sum(ground_labels[:,j])==0:
            del_ind.append(j)
    del_ind = np.array(del_ind)
    ground_labels = np.delete(ground_labels,del_ind,axis=1)
    preded_probs = test_prob_pertpart[tuple(benign_inds),:]
    preded_probs = np.delete(preded_probs,del_ind,axis=1)
    auc_score_clc_bn = roc_auc_score(ground_labels, preded_probs)
    
    ground_labels_adv = y_adv_pertpart[tuple(adv_inds),:]
    del_ind = []
    for j in range(labels_nums):
        if np.sum(ground_labels_adv[:,j])==0:
            del_ind.append(j)
    del_ind = np.array(del_ind)
    ground_labels_adv = np.delete(ground_labels_adv,del_ind,axis=1)
    preded_probs_adv = adv_prob_pertpart[tuple(adv_inds),:]
    preded_probs_adv = np.delete(preded_probs_adv,del_ind,axis=1)
    auc_score_clc_adv = roc_auc_score(ground_labels_adv, preded_probs_adv)
    print("Threshold {}:) auc_score_clc_bn:{}, auc_score_clc_adv:{}".format(eva_thresh[i],auc_score_clc_bn, auc_score_clc_adv))
    
  sess.close()