cifar10_DistillationNet_Cons-Def_pgd_defensexx.py

"""
This tutorial shows how to implement Cons-Def against PGD white-box attacks.
Xintao Ding
School of Computer and Information, Anhui Normal University
xintaoding@163.com
"""
# pylint: disable=missing-docstring
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import logging
import numpy as np
import tensorflow as tf

from cleverhans.augmentation import random_horizontal_flip, random_shift
from cleverhans.compat import flags
from cleverhans.dataset import CIFAR10
#from cleverhans.model_zoo.all_convolutional import ModelAllConvolutional#Uncomments this, if use the all conv network
from cleverhans.model_zoo.four_conv2FC import Model4Convolutional2FC#using the network of 4 conv + 2 fully connected layers

from cleverhans.attacks import ProjectedGradientDescent
from cleverhans.utils import AccuracyReport, set_log_level
from cleverhans.utils_tf import model_eval

from cleverhans.data_exten_mulpro import data_exten#Added by Ding
from sklearn.metrics import roc_curve, roc_auc_score#Added by Ding
#import matplotlib.pyplot as plt
#from tensorflow.python import pywrap_tensorflow
import multiprocessing

FLAGS = flags.FLAGS

NB_EPOCHS = 6
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, nb_epochs=NB_EPOCHS, 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
  """

  # Object used to keep track of (and return) key accuracies
  report = AccuracyReport()

  # Set TF random seed to improve reproducibility
  tf.set_random_seed(1234)

  # Set logging level to see debug information
  set_log_level(logging.DEBUG)

  # 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')

  # Use Image Parameters
  print("y_train_shape:{}".format(y_train.shape))#########################################
  img_rows, img_cols, nchannels = x_test.shape[1:4]
  nb_classes = y_train.shape[1]
  
  print("nb_classes:{}".format(nb_classes))#########################################
#  assert 1==2

  # 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}
  pgd_params = {
      'eps': 0.03,
      'eps_iter':0.0075,
      'rand_init':1,
      'clip_min': 0.,
      'clip_max': 1.
  }
#  rng = np.random.RandomState([2017, 8, 30])

#    model = ModelAllConvolutional('model1', nb_classes, nb_filters,
#                                  input_shape=[32, 32, 3])#Load the model graph of all conv
  net_height=32
  net_width=32
#Load the model graph of 4 conv and 2 fc layers, Revised by Ding==========
  model = Model4Convolutional2FC('model1', nb_classes, nb_filters, input_shape=[net_height, net_width, nchannels])
  preds = model.get_logits(x)
  probs = model.get_probs(x)
#    loss = CrossEntropy(model, smoothing=label_smoothing)

  #########################################Added by Ding  
  print("aaaaaaaaaaaaaaaaa=+++++++++++++++++++++++++++++")
  saver = tf.train.Saver() 
  print("aaaaaaaaaaaaaaaaa=--------------------------------")
  saver.restore(sess,'models/cifar10/cifar10_dtcnn_train_epoch89')
  print("aaaaaaaaaaaaaaaaa=************************************")

    # Initialize the Projected Gradient Descent Method (PGDM) attack object and
    # graph
  pgd = ProjectedGradientDescent(model, sess=sess)
  adv_x = pgd.generate(x, **pgd_params)

  adv = np.zeros((test_end, img_rows, img_cols, nchannels),dtype=np.float32)
  craft_bs = 100
  val_steps = int(test_end / craft_bs)
  for i in range(val_steps):
      feed_dict = {x: x_test[i*craft_bs:(i+1)*craft_bs,:,:,:]}
      adv_bat = sess.run(adv_x,feed_dict=feed_dict)
      adv[i*craft_bs:(i+1)*craft_bs,:,:,:] = adv_bat
      
#      print("adv_bat:{},{}".format(np.sum(adv_bat),adv_bat))

#      xxx=(adv[60,:,:,:])*255
#      xxx=xxx.astype(np.uint8)
#      plt.imshow(xxx)
#      xxx=(x_test[60,:,:,:])*255
#      xxx=xxx.astype(np.uint8)
#      plt.imshow(xxx)

      #      temp = sess.run(adv_x,feed_dict=feed_dict)
#      adv[i*craft_bs:(i+1)*craft_bs,:,:,:] = np.round(temp*128.)*(1./128)
#      print("len:{}".format(len(np.unique(adv[0,:,:,0]))))
#    adv = adv_x.eval(feed_dict=feed_dict)
  print("advaaaaaaaaaaaaaaaaaaaaaaaaaaa:{}".format(np.sum(adv-x_test)))

#  np.save("cifar10_DistillationNet_augmodel_pgd_10000adv",adv)#save advs produced on clean model:

# Evaluate the accuracy of the MNIST model on 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))
    
  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 classified
          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))
  
  #########################################
  x_test = np.round(x_test*256)/256.0
  x_test40 = np.zeros((len(x_test),8+img_rows,8+img_rows,3),dtype=np.float32)
  x_testcrop = np.zeros((len(x_test),img_rows,img_cols,3),dtype=np.float32)
  x_test40[:,4:36,4:36,:] = x_test
  x_test40[:, 4:36, :4, :]=x_test[:, :, [3,2,1,0], :]#reflect
  x_test40[:, 4:36, 36:, :]=x_test[:, :, [31,30,29,28], :]
  x_test40[:, :4, :, :]=x_test40[:, [7,6,5,4], :, :]
  x_test40[:, 36:, :, :]=x_test40[:, [35,34,33,32], :, :]
  adv = np.round(adv*256)/256.0
  adv40 = np.zeros((len(adv),8+img_rows,8+img_rows,3),dtype=np.float32)
  advcrop = np.zeros((len(adv),img_rows,img_cols,3),dtype=np.float32)
  adv40[:, 4:36, 4:36, :] = adv
  adv40[:, 4:36, :4, :]=adv[:, :, [3,2,1,0], :]#reflect
  adv40[:, 4:36, 36:, :]=adv[:, :, [31,30,29,28], :]
  adv40[:, :4, :, :]=adv40[:, [7,6,5,4], :, :]
  adv40[:, 36:, :, :]=adv40[:, [35,34,33,32], :, :]
  for i in range(len(adv)):
      tf_image = adv40[i,:,:,:]
      test_image = x_test40[i,:,:,:]
      lu1 = np.random.randint(0,8)
      lu2 = np.random.randint(0,8)
      advcrop[i,:,:,:] = tf_image[lu1:lu1+img_rows,lu2:lu2+img_cols,:]
      x_testcrop[i,:,:,:] = test_image[lu1:lu1+img_rows,lu2:lu2+img_cols,:]
  adv = advcrop
  x_test = x_testcrop

#  np.save("cifar10_DistillationNet_augmodel_fgsmlinf_1000adv",adv[:1000,:,:,:])#save advs produced on augmented model:
    
  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)
    
  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[2*i*batch_size:(2*i+1)*batch_size,:], 
                                            batch_size, base_range,nb_classes,net_height, net_width,nchannels,
                                            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,nb_classes,net_height, net_width,nchannels,
                                            rt_res_adv2))
      p3 = multiprocessing.Process(target=data_exten,args=(x_test[2*i*batch_size:(2*i+1)*batch_size,:,:,:],
                                            y_test[2*i*batch_size:(2*i+1)*batch_size,:], 
                                            batch_size, base_range,nb_classes,net_height, net_width,nchannels,
                                            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,nb_classes,net_height, net_width,nchannels,
                                            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[2*i*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[2*i*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,:]

#for test accuracy on legitimate examples extended by x_test
      feed_dict = {x: adv[2*i*batch_size:2*(i+1)*batch_size,:,:,:]}
      adv_prob_legit[2*i*batch_size:2*(i+1)*batch_size,:] = sess.run(probs,feed_dict = feed_dict)
      feed_dict = {x: x_test[2*i*batch_size:2*(i+1)*batch_size,:,:,:]}
      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):
#        if j%10 == 0:
#            print("j:{}".format(j))
        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 = {x: val_x_bat}
        logits_bat = sess.run(preds, feed_dict=feed_dict)
        
        feed_dict = {x: val_adv_bat}#range to [-0.5, 0.5]
        logits_adv_bat = sess.run(preds,feed_dict=feed_dict)
#        loss_adv = sess.run(loss_x,feed_dict=feed_dict)

        y_test_prob = sess.run(probs,feed_dict = {x: val_x_bat})
        y_adv_prob = sess.run(probs,feed_dict = {x: val_adv_bat})

        #for tensorflow1.2, keep_prob cannot be defined as placeholder, it must be a scalar and it needn't be fed to session

        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#there is a inconsensus detection results of the 27 perturbations
      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(nb_classes):
        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(nb_classes):
        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)
#    auc_score_clc_adv = roc_auc_score(y_adv_pertpart[tuple(adv_inds),:], y_pertpart_prob_adv[tuple(adv_inds),:])
      print("(Threshold {}:) auc_score_clc_bn:{},auc_score_clc_adv:{}".format(eva_thresh[i],auc_score_clc_bn, auc_score_clc_adv))
  
  # Close TF session
  sess.close()



def main(argv=None):
  """
  Run the tutorial using command line flags.
  """
  from cleverhans_tutorials import check_installation
  check_installation(__file__)

  cifar10_tutorial(nb_epochs=FLAGS.nb_epochs, 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('nb_epochs', NB_EPOCHS,
                       'Number of epochs to train model')
  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()