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DTPI-FGSM++_for_NT.py
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DTPI-FGSM++_for_NT.py
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"""Implementation of sample attack."""
# coding: utf-8
#/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from utils import *
from numpy import pi, exp, sqrt
from attack_method import *
from tqdm import tqdm
from tensorpack import TowerContext
from nets import inception_v3, inception_v4, inception_resnet_v2, resnet_v2, resnet_v1
# from tensorpack.tfutils import get_model_loader
from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
import os
import cv2
from PIL import ImageFilter
slim = tf.contrib.slim
tf.flags.DEFINE_string('checkpoint_path', './models', 'Path to checkpoint for inception network.')
tf.flags.DEFINE_string('input_csv', 'dataset/dev_dataset.csv', 'Input directory with images.')
tf.flags.DEFINE_string('input_dir', 'dataset/images/', 'Input directory with images.')
tf.flags.DEFINE_string('output_dir', 'output/', 'Output directory with images.')
tf.flags.DEFINE_float('max_epsilon', 16.0, 'Maximum size of adversarial perturbation.')
tf.flags.DEFINE_float('num_classes', 1001, 'Maximum size of adversarial perturbation.')
tf.flags.DEFINE_integer('num_iter', 20, 'Number of iterations.')
tf.flags.DEFINE_integer('image_width', 299, 'Width of each input images.')
tf.flags.DEFINE_integer('image_height', 299, 'Height of each input images.')
tf.flags.DEFINE_integer('image_resize', 330, 'Height of each input images.')
tf.flags.DEFINE_integer('batch_size', 5, 'How many images process at one time.')
tf.flags.DEFINE_float('momentum', 1.0, 'Momentum.')
tf.flags.DEFINE_float('prob', 0.7, 'probability of using diverse inputs.')
tf.flags.DEFINE_float('amplification_factor', 10.0, 'To amplifythe step size.')
tf.flags.DEFINE_float('project_factor', 0.8, 'To control the weight of project term.')
tf.flags.DEFINE_float('temperature', 1.5, 'To soften the output probability distribution.')
FLAGS = tf.flags.FLAGS
num_of_K = 1.5625 # 100 / 64
T_kern = gkern(5, 3)
P_kern, kern_size = project_kern(3)
model_checkpoint_map = {
'inception_v3': os.path.join(FLAGS.checkpoint_path, 'inception_v3.ckpt'),
'adv_inception_v3': os.path.join(FLAGS.checkpoint_path, 'adv_inception_v3.ckpt'),
'ens3_adv_inception_v3': os.path.join(FLAGS.checkpoint_path, 'ens3_adv_inception_v3.ckpt'),
'ens4_adv_inception_v3': os.path.join(FLAGS.checkpoint_path, 'ens4_adv_inception_v3.ckpt'),
'inception_v4': os.path.join(FLAGS.checkpoint_path, 'inception_v4.ckpt'),
'inception_resnet_v2': os.path.join(FLAGS.checkpoint_path, 'inception_resnet_v2_2016_08_30.ckpt'),
'ens_adv_inception_resnet_v2': os.path.join(FLAGS.checkpoint_path, 'ens_adv_inception_resnet_v2.ckpt'),
'resnet_v2_101': os.path.join(FLAGS.checkpoint_path, 'resnet_v2_101.ckpt'),
'vgg_16': os.path.join(FLAGS.checkpoint_path,'vgg_16.ckpt'),
'resnet_v2_152': os.path.join(FLAGS.checkpoint_path,'resnet_v2_152.ckpt'),
'adv_inception_resnet_v2': os.path.join(FLAGS.checkpoint_path, 'adv_inception_resnet_v2.ckpt'),
'resnet_v2_50': os.path.join(FLAGS.checkpoint_path,'resnet_v2_50.ckpt')}
def graph(adv, y, t_y, i, x_max, x_min, grad, amplification):
target_one_hot = tf.one_hot(t_y, 1001)
eps = 2.0 * FLAGS.max_epsilon / 255.0
num_iter = FLAGS.num_iter
alpha = eps / num_iter
momentum = FLAGS.momentum
alpha_beta = alpha * FLAGS.amplification_factor
gamma = alpha_beta * FLAGS.project_factor
num_classes = 1001
# with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
# logits_v3, end_points_v3 = inception_v3.inception_v3(
# input_diversity(FLAGS, adv), num_classes=num_classes, is_training=False, reuse = True)
# # auxlogits_v3 = end_points_v3['AuxLogits']
with slim.arg_scope(inception_v4.inception_v4_arg_scope()):
logits_v4, end_points_v4 = inception_v4.inception_v4(
input_diversity(FLAGS, adv), num_classes=num_classes, is_training=False, reuse = True)
# auxlogits_v4 = end_points_v4['AuxLogits']
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits_resnet_152, end_points_resnet = resnet_v2.resnet_v2_152(
input_diversity(FLAGS, adv), num_classes=num_classes, is_training=False, reuse = True)
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits_resnet_101, end_points_resnet_101 = resnet_v2.resnet_v2_101(
input_diversity(FLAGS, adv), num_classes=num_classes, is_training=False, reuse = True)
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits_resnet_50, end_points_resnet_50 = resnet_v2.resnet_v2_50(
input_diversity(FLAGS, adv), num_classes=num_classes, is_training=False, reuse = True)
with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
logits_Incres, end_points_IR = inception_resnet_v2.inception_resnet_v2(
input_diversity(FLAGS, adv), num_classes=num_classes, is_training=False, reuse = True)
# auxlogits_Incres = end_points_IR['AuxLogits']
logits = (logits_v4 + logits_resnet_152 + logits_resnet_101 + logits_Incres + logits_resnet_50) / 5.0 / FLAGS.temperature
target_cross_entropy = tf.losses.softmax_cross_entropy(target_one_hot,
logits,
label_smoothing=0.0,
weights=1.0)
noise = tf.gradients(target_cross_entropy, adv)[0]
noise = tf.nn.depthwise_conv2d(noise, T_kern, strides=[1, 1, 1, 1], padding='SAME')
# Project cut noise
amplification += alpha_beta * tf.sign(noise)
cut_noise = tf.clip_by_value(abs(amplification) - eps, 0.0, 10000.0) * tf.sign(amplification)
projection = gamma * tf.sign(project_noise(cut_noise, P_kern, kern_size))
adv = adv - alpha_beta * tf.sign(noise) - projection
adv = tf.clip_by_value(adv, x_min, x_max)
i = tf.add(i, 1)
return adv, y, t_y, i, x_max, x_min, noise, amplification
def stop(adv, y, t_y, i, x_max, x_min, grad, total_grad):
num_iter = FLAGS.num_iter
return tf.less(i, num_iter)
def main(_):
# Images for inception classifier are normalized to be in [-1, 1] interval,
# eps is a difference between pixels so it should be in [0, 2] interval.
# Renormalizing epsilon from [0, 255] to [0, 2].
mean_pert = 0.0
eps = 2.0 * FLAGS.max_epsilon / 255.0
num_classes = 1001
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
sum_fd1, sum_fd2, sum_fd3, sum_adv_v3, \
sum_ens3_adv_v3, sum_ens4_adv_v3, sum_ensadv_res_v2 = 0, 0, 0, 0, 0, 0, 0
sum_v3, sum_v4, sum_res152, sum_res101, sum_res50, sum_Incres, sum_ensemble = 0,0,0,0,0,0,0
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
# Prepare graph
adv_img = tf.placeholder(tf.float32, shape = batch_shape)
y = tf.placeholder(tf.int32, shape = batch_shape[0])
t_y = tf.placeholder(tf.int32, shape = batch_shape[0])
x_max = tf.clip_by_value(adv_img + eps, -1.0, 1.0)
x_min = tf.clip_by_value(adv_img - eps, -1.0, 1.0)
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
logits_v3, end_points_v3 = inception_v3.inception_v3(
adv_img, num_classes=num_classes, is_training=False)
pre_v3 = tf.argmax(logits_v3, 1)
with slim.arg_scope(inception_v4.inception_v4_arg_scope()):
logits_v4, end_points_v4 = inception_v4.inception_v4(
adv_img, num_classes=num_classes, is_training=False)
pre_v4 = tf.argmax(logits_v4, 1)
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits_resnet_152, end_points_resnet = resnet_v2.resnet_v2_152(
adv_img, num_classes=num_classes, is_training=False)
pre_resnet_152 = tf.argmax(logits_resnet_152, 1)
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits_resnet_101, end_points_resnet_101 = resnet_v2.resnet_v2_101(
adv_img, num_classes=num_classes, is_training=False)
pre_resnet_101 = tf.argmax(logits_resnet_101, 1)
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits_resnet_50, end_points_resnet_50 = resnet_v2.resnet_v2_50(
adv_img, num_classes=num_classes, is_training=False)
pre_resnet_50 = tf.argmax(logits_resnet_50, 1)
with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
logits_Incres, end_points_IR = inception_resnet_v2.inception_resnet_v2(
adv_img, num_classes=num_classes, is_training=False)
pre_Inc_res = tf.argmax(logits_Incres, 1)
pre_ensemble_logit = tf.argmax((logits_v4 + logits_resnet_152 + logits_resnet_101 + logits_resnet_50 + logits_Incres), 1)
sum_v3, sum_v4, sum_res152, sum_res101, sum_res50, sum_Incres, sum_ensemble = 0, 0, 0, 0, 0, 0, 0
i = tf.constant(0)
grad = tf.zeros(shape=batch_shape)
amplification = tf.zeros(shape=batch_shape)
x_adv, _, _, _, _, _, _, _ = tf.while_loop(stop, graph, [adv_img, y, t_y, i, x_max, x_min, grad, amplification])
# Run computation
s1 = tf.train.Saver(slim.get_model_variables(scope='InceptionV3'))
s5 = tf.train.Saver(slim.get_model_variables(scope='InceptionV4'))
s6 = tf.train.Saver(slim.get_model_variables(scope='InceptionResnetV2'))
s8 = tf.train.Saver(slim.get_model_variables(scope='resnet_v2_152'))
s9 = tf.train.Saver(slim.get_model_variables(scope='resnet_v2_101'))
s10 = tf.train.Saver(slim.get_model_variables(scope='resnet_v2_50'))
with tf.Session() as sess:
s1.restore(sess, model_checkpoint_map['inception_v3'])
s5.restore(sess, model_checkpoint_map['inception_v4'])
s6.restore(sess, model_checkpoint_map['inception_resnet_v2'])
s8.restore(sess, model_checkpoint_map['resnet_v2_152'])
s9.restore(sess, model_checkpoint_map['resnet_v2_101'])
s10.restore(sess, model_checkpoint_map['resnet_v2_50'])
import pandas as pd
dev = pd.read_csv(FLAGS.input_csv)
for idx in tqdm(range(0, 1000 // FLAGS.batch_size)):
images, filenames, True_label, Target_label = load_images(FLAGS.input_dir, dev, idx * FLAGS.batch_size, batch_shape)
my_adv_images = sess.run(x_adv, feed_dict={adv_img: images, y: True_label, t_y: Target_label}).astype(np.float32)
pre_v3_, pre_v4_, pre_resnet152_, pre_resnet101_, pre_resnet50_, pre_Inc_res_, pre_ensemble_ \
= sess.run([pre_v3, pre_v4, pre_resnet_152, pre_resnet_101, pre_resnet_50, pre_Inc_res, pre_ensemble_logit,
],
feed_dict = {adv_img: my_adv_images})
sum_v3 += (pre_v3_ == Target_label).sum()
sum_v4 += (pre_v4_ == Target_label).sum()
sum_res152 += (pre_resnet152_ == Target_label).sum()
sum_res101 += (pre_resnet101_ == Target_label).sum()
sum_res50 += (pre_resnet50_ == Target_label).sum()
sum_Incres += (pre_Inc_res_ == Target_label).sum()
sum_ensemble += (pre_ensemble_ == Target_label).sum()
save_images(my_adv_images, filenames, FLAGS.output_dir)
print('sum_v3 = {}'.format(sum_v3))
print('sum_v4 = {}'.format(sum_v4))
print('sum_res2 = {}'.format(sum_res152))
print('sum_res1 = {}'.format(sum_res101))
print('sum_res1 = {}'.format(sum_res50))
print('sum_Incres_v2 = {}'.format(sum_Incres))
print('sum_ensmeble = {}'.format(sum_ensemble))
if __name__ == '__main__':
tf.app.run()