cifar10_vgg16_Cons-Def_blackdefense.py

"""
This tutorial shows how to generate transferable attacks from adversarial examples of the source model
The target model is VGG16.
The target model requires the input with the size of 160*160, therefore, the source examples are resized for implementation.
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.data_exten_mulpro import data_exten
from sklearn.metrics import roc_curve, roc_auc_score
import cv2
import multiprocessing

labels_nums = 10  #  the number of labels
batch_size = 20  #
resize_height = 32  # Cifar10 size
resize_width = 32
#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_height= 160 #vgg16 size
net_width = 160
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')
#test data
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()

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=+++++++++++++++++++++++++++++")
#########################################
  adv = np.load("cifar10_ResNet50_augmodel_deepfool_10000adv.npy")#source attacks
  adv_cnn = np.zeros((len(adv),net_height,net_width,depths))
  for i in range(len(adv)):
      advi = adv[i,:,:,:]
      adv_cnn[i,:,:,:] = cv2.resize(advi,(net_height,net_width))
#      plt.imshow(np.uint8(adv_cnn[i,:,:,:]*255)) 
  adv = adv_cnn    
      
  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)
  
  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)
  
      adv_bat = adv[i*batch_size:(i+1)*batch_size,:,:,:]

      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
      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)
  
 #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
  left_cols=np.arange(pad_size)
  left_cols=left_cols[::-1]
  right_cols=np.arange(net_height-pad_size,net_height)
  right_cols=right_cols[::-1]
  top_rows=np.arange(pad_size,pad_size*2)
  top_rows=top_rows[::-1]
  foot_rows=np.arange(net_height,net_height+pad_size)
  foot_rows=foot_rows[::-1]
#    x_test=np.pad(x_test,((0,0),(pad_size,pad_size),(pad_size,pad_size),(0,0)),'reflect')#very slow, and memery consume is too large
#    x_testcrop = np.zeros((len(x_test),net_height,net_width,3),dtype=np.float32)
#    adv = np.round(adv*256)/256.0#very slow, and memery consume is too large
#    adv = np.pad(adv,((0,0),(pad_size,pad_size),(pad_size,pad_size),(0,0)),'reflect')
  temp_test = np.zeros((net_height+2*pad_size,net_width+2*pad_size,3))
  temp_adv = np.zeros((net_height+2*pad_size,net_width+2*pad_size,3))
#    advcrop = np.zeros((len(adv),net_height,net_width,3),dtype=np.float32)
  for i in range(len(adv)):
      tf_image = adv[i,:,:,:]
      tf_image = np.round(tf_image*256)/256.0
      temp_adv[pad_size:net_height+pad_size,pad_size:net_width+pad_size,:] = tf_image
      temp_adv[pad_size:net_height+pad_size, :pad_size, :]=tf_image[:, left_cols, :]#reflect left
      temp_adv[pad_size:net_height+pad_size, net_height+pad_size:, :]=tf_image[:, right_cols, :]#reflect right
      temp_adv[:pad_size, :, :]=temp_adv[top_rows, :, :]
      temp_adv[net_height+pad_size:, :, :]=temp_adv[foot_rows, :, :]
      tf_image = temp_adv
#      tf_image = np.pad(tf_image,((pad_size,pad_size),(pad_size,pad_size),(0,0)),'reflect')
      test_image = x_test[i,:,:,:]
      temp_test[pad_size:net_height+pad_size,pad_size:net_width+pad_size,:] = test_image
      temp_test[pad_size:net_height+pad_size, :pad_size, :]=test_image[:, left_cols, :]#reflect left
      temp_test[pad_size:net_height+pad_size, net_height+pad_size:, :]=test_image[:, right_cols, :]#reflect right
      temp_test[:pad_size, :, :]=temp_test[top_rows, :, :]
      temp_test[net_height+pad_size:, :, :]=temp_test[foot_rows, :, :]
      test_image = temp_test
#      test_image = np.pad(test_image,((pad_size,pad_size),(pad_size,pad_size),(0,0)),'reflect')
      lu1 = np.random.randint(0,pad_size*2)
      lu2 = np.random.randint(0,pad_size*2)
      adv[i,:,:,:] = tf_image[lu1:lu1+net_height,lu2:lu2+net_width,:]
      x_test[i,:,:,:] = test_image[lu1:lu1+net_height,lu2:lu2+net_width,:]
  
  batch_size = 20  #
  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()