imgnet_resnet50_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.resnet_v1 as resnet_v1
import tensorflow.contrib.slim as slim
from create_tf_record import get_example_nums,read_records,get_batch_images

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

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

batch_size = 20  #
LEARNING_RATE = 0.001
CLEAN_TRAIN = True
BACKPROP_THROUGH_ATTACK = False
NB_FILTERS = 64

labels_nums = 10  #  the number of labels
resize_height = 299  # imagenet size
resize_width = 299
net_height = 224 #ResNet50 size
net_width = 224
depths = 3


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')
is_training = tf.placeholder(tf.bool, name='is_training')

#test data
val_record_file='data/caffe_ilsvrc12_record/val299.tfrecords'
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, type='normalization')
val_images, val_labels = read_records([val_record_file], resize_height, resize_width, type='centralization')
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=val_images_batch[:,37:261,37:261,:]
val_images_batch = tf.rint(val_images_batch*256.)*(1./256)

# Define the model:
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
    out, end_points = resnet_v1.resnet_v1_50(inputs=input_images, num_classes=labels_nums, is_training=is_training)
#    out, end_points = inception_v3.inception_v3(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))
accuracy = tf.equal(tf.argmax(out, 1), tf.argmax(input_labels, 1))

saver = tf.train.Saver()

eps = 0.03
clip_min = -0.5
clip_max = 0.5
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

  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))
  
  loss = loss_fn(labels=y, logits=logits)
  # Compute loss
  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,
                        tf.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)

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)

  #########################################Added by Ding  
    print("aaaaaaaaaaaaaaaaa=+++++++++++++++++++++++++++++")
    saver.restore(sess,'models/caffe_ilsvrc12/resnet50_best_models_1393000_0.7667.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#Ranged in [0, 1]
        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)
    
    percent_perturbed = np.mean(np.sum((adv - x_test)**2, axis=(1, 2, 3))**.5)

 #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

    dsae=0
    kk=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))
    
    pad_size=22
    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
    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):
      if i%10 == 0:
          print("i:{}".format(i))
      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,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[2*i*batch_size:(2*i+1)*batch_size,:,:,:],
                                            y_test[2*i*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[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 = {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):
#        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 = {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}#range to [-0.5, 0.5]
        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))

    logits = np.argmax(logits,axis=1)
    logits_adv = np.argmax(logits_adv,axis=1)
    y_test = np.argmax(y_test,axis=1)
    acc = np.sum(np.equal(logits,y_test))/len(y_test)
    acc_adv = np.sum(np.equal(logits_adv,y_test))/len(y_test)
    print('Test accuracy on legitimate test examples: %0.4f' % (acc))
    print('Test accuracy on adversarial test examples: %0.4f' % (acc_adv))
    
    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(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)
#    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))
    sess.close()
  #########################################