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NAA.py
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NAA.py
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"""Implementation of attack."""
# coding: utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.keras.utils import to_categorical
import time
import utils
import os
from scipy import misc
from scipy import ndimage
import PIL
import io
slim = tf.contrib.slim
tf.flags.DEFINE_string('model_name', 'inception_v3', 'The Model used to generate adv.')
tf.flags.DEFINE_string('attack_method', 'NAA', 'The name of attack method.')
tf.flags.DEFINE_string('layer_name','InceptionV3/InceptionV3/Mixed_5b/concat','The layer to be attacked.')
tf.flags.DEFINE_string('input_dir', './dataset/images/', 'Input directory with images.')
tf.flags.DEFINE_string('output_dir', './adv/NAA/', 'Output directory with images.')
tf.flags.DEFINE_float('max_epsilon', 16.0, 'Maximum size of adversarial perturbation.')
tf.flags.DEFINE_integer('num_iter', 10, 'Number of iterations.')
tf.flags.DEFINE_float('alpha', 1.6, 'Step size.')
tf.flags.DEFINE_integer('batch_size', 20, 'How many images process at one time.')
tf.flags.DEFINE_float('momentum', 1.0, 'Momentum.')
tf.flags.DEFINE_string('GPU_ID', '0', 'which GPU to use.')
"""parameter for DIM"""
tf.flags.DEFINE_integer('image_size', 299, 'size of each input images.')
tf.flags.DEFINE_integer('image_resize', 331, 'size of each diverse images.')
tf.flags.DEFINE_float('prob', 0.7, 'Probability of using diverse inputs.')
"""parameter for PIM"""
tf.flags.DEFINE_float('amplification_factor', 2.5, 'To amplifythe step size.')
tf.flags.DEFINE_float('gamma', 0.5, 'The gamma parameter.')
tf.flags.DEFINE_integer('Pkern_size', 3, 'Kernel size of PIM.')
"""parameter for NAA"""
tf.flags.DEFINE_float('ens', 30.0, 'Number of aggregated n.')
FLAGS = tf.flags.FLAGS
os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.GPU_ID
"""obtain the feature map of the target layer"""
def get_opt_layers(layer_name):
opt_operations = []
#shape=[FLAGS.batch_size,FLAGS.image_size,FLAGS.image_size,3]
operations = tf.get_default_graph().get_operations()
for op in operations:
if layer_name == op.name:
opt_operations.append(op.outputs[0])
shape=op.outputs[0][:FLAGS.batch_size].shape
break
return opt_operations,shape
def get_NAA_loss(opt_operations,weights,base_feature):
loss = 0
gamma = 1.0
for layer in opt_operations:
ori_tensor = layer[:FLAGS.batch_size]
adv_tensor = layer[FLAGS.batch_size:]
attribution = (adv_tensor-base_feature)*weights
#attribution = adv_tensor*weights
blank = tf.zeros_like(attribution)
positive = tf.where(attribution >= 0, attribution, blank)
negative = tf.where(attribution < 0, attribution, blank)
## Transformation: Linear transformation performs the best
positive = positive
negative = negative
##
balance_attribution = positive + gamma*negative
loss += tf.reduce_sum(balance_attribution) / tf.cast(tf.size(layer), tf.float32)
loss = loss / len(opt_operations)
return loss
def normalize(grad,opt=2):
if opt==0:
nor_grad=grad
elif opt==1:
abs_sum=np.sum(np.abs(grad),axis=(1,2,3),keepdims=True)
nor_grad=grad/abs_sum
elif opt==2:
square = np.sum(np.square(grad),axis=(1,2,3),keepdims=True)
nor_grad=grad/np.sqrt(square)
return nor_grad
def project_kern(kern_size):
kern = np.ones((kern_size, kern_size), dtype=np.float32) / (kern_size ** 2 - 1)
kern[kern_size // 2, kern_size // 2] = 0.0
kern = kern.astype(np.float32)
stack_kern = np.stack([kern, kern, kern]).swapaxes(0, 2)
stack_kern = np.expand_dims(stack_kern, 3)
return stack_kern, kern_size // 2
def project_noise(x, stack_kern, kern_size):
x = tf.pad(x, [[0,0],[kern_size,kern_size],[kern_size,kern_size],[0,0]], "CONSTANT")
x = tf.nn.depthwise_conv2d(x, stack_kern, strides=[1, 1, 1, 1], padding='VALID')
return x
def input_diversity(input_tensor):
"""Input diversity: https://arxiv.org/abs/1803.06978"""
rnd = tf.random_uniform((), FLAGS.image_size, FLAGS.image_resize, dtype=tf.int32)
rescaled = tf.image.resize_images(input_tensor, [rnd, rnd], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
h_rem = FLAGS.image_resize - rnd
w_rem = FLAGS.image_resize - rnd
pad_top = tf.random_uniform((), 0, h_rem, dtype=tf.int32)
pad_bottom = h_rem - pad_top
pad_left = tf.random_uniform((), 0, w_rem, dtype=tf.int32)
pad_right = w_rem - pad_left
padded = tf.pad(rescaled, [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]], constant_values=0.)
padded.set_shape((input_tensor.shape[0], FLAGS.image_resize, FLAGS.image_resize, 3))
ret=tf.cond(tf.random_uniform(shape=[1])[0] < tf.constant(FLAGS.prob), lambda: padded, lambda: input_tensor)
ret = tf.image.resize_images(ret, [FLAGS.image_size, FLAGS.image_size],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return ret
P_kern, kern_size = project_kern(FLAGS.Pkern_size)
def main(_):
if FLAGS.model_name in ['vgg_16','vgg_19', 'resnet_v1_50','resnet_v1_152']:
eps = FLAGS.max_epsilon
alpha = FLAGS.alpha
else:
eps = 2.0 * FLAGS.max_epsilon / 255.0
alpha = FLAGS.alpha * 2.0 / 255.0
num_iter = FLAGS.num_iter
momentum = FLAGS.momentum
image_preprocessing_fn = utils.normalization_fn_map[FLAGS.model_name]
inv_image_preprocessing_fn = utils.inv_normalization_fn_map[FLAGS.model_name]
batch_shape = [FLAGS.batch_size, FLAGS.image_size, FLAGS.image_size, 3]
checkpoint_path = utils.checkpoint_paths[FLAGS.model_name]
layer_name=FLAGS.layer_name
with tf.Graph().as_default():
# Prepare graph
ori_input = tf.placeholder(tf.float32, shape=batch_shape)
adv_input = tf.placeholder(tf.float32, shape=batch_shape)
num_classes = 1000 + utils.offset[FLAGS.model_name]
label_ph = tf.placeholder(tf.float32, shape=[FLAGS.batch_size*2,num_classes])
accumulated_grad_ph = tf.placeholder(dtype=tf.float32, shape=batch_shape)
amplification_ph = tf.placeholder(dtype=tf.float32, shape=batch_shape)
network_fn = utils.nets_factory.get_network_fn(FLAGS.model_name, num_classes=num_classes, is_training=False)
x=tf.concat([ori_input,adv_input],axis=0)
# whether using DIM or not
if 'DI' in FLAGS.attack_method:
logits, end_points = network_fn(input_diversity(x))
else:
logits, end_points = network_fn(x)
problity=tf.nn.softmax(logits,axis=1)
pred = tf.argmax(logits, axis=1)
one_hot = tf.one_hot(pred, num_classes)
entropy_loss = tf.losses.softmax_cross_entropy(one_hot[:FLAGS.batch_size], logits[FLAGS.batch_size:])
opt_operations,shape = get_opt_layers(layer_name)
weights_ph = tf.placeholder(dtype=tf.float32, shape=shape)
base_feature = tf.placeholder(dtype=tf.float32, shape=shape)
weights_tensor = tf.gradients(tf.nn.softmax(logits) * label_ph, opt_operations[0])[0]
loss = get_NAA_loss(opt_operations,weights_ph,base_feature)
gradient=tf.gradients(loss,adv_input)[0]
noise = gradient
adv_input_update = adv_input
amplification_update = amplification_ph
# the default optimization process with momentum
noise = noise / tf.reduce_mean(tf.abs(noise), [1, 2, 3], keep_dims=True)
noise = momentum * accumulated_grad_ph + noise
# whether using PIM or not
if 'PI' in FLAGS.attack_method:
# amplification factor
alpha_beta = alpha * FLAGS.amplification_factor
gamma = FLAGS.gamma * alpha_beta
# Project cut noise
amplification_update += alpha_beta * tf.sign(noise)
cut_noise = tf.clip_by_value(abs(amplification_update) - eps, 0.0, 10000.0) * tf.sign(amplification_update)
projection = gamma * tf.sign(project_noise(cut_noise, P_kern, kern_size))
amplification_update += projection
adv_input_update = adv_input_update + alpha_beta * tf.sign(noise) + projection
else:
adv_input_update = adv_input_update + alpha * tf.sign(noise)
saver=tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess,checkpoint_path)
count=0
for images,names,labels in utils.load_image(FLAGS.input_dir, FLAGS.image_size,FLAGS.batch_size):
count+=FLAGS.batch_size
if count%100==0:
print("Generating:",count)
images_tmp=image_preprocessing_fn(np.copy(images))
if FLAGS.model_name in ['resnet_v1_50','resnet_v1_152','vgg_16','vgg_19']:
labels=labels-1
# obtain true label
labels= to_categorical(np.concatenate([labels,labels],axis=-1),num_classes)
images_adv=images
images_adv=image_preprocessing_fn(np.copy(images_adv))
grad_np=np.zeros(shape=batch_shape)
amplification_np=np.zeros(shape=batch_shape)
weight_np = np.zeros(shape=shape)
for i in range(num_iter):
if i==0:
if FLAGS.ens == 0:
images_tmp2 = image_preprocessing_fn(np.copy(images))
w, feature = sess.run([weights_tensor, opt_operations[0]],
feed_dict={ori_input: images_tmp2, adv_input: images_tmp2,label_ph: labels})
weight_np =w[:FLAGS.batch_size]
for l in range(int(FLAGS.ens)):
x_base = np.array([0.0,0.0,0.0])
x_base = image_preprocessing_fn(x_base)
images_tmp2 = image_preprocessing_fn(np.copy(images))
images_tmp2 += np.random.normal(size = images.shape, loc=0.0, scale=0.2)
images_tmp2 = images_tmp2*(1 - l/FLAGS.ens)+ (l/FLAGS.ens)*x_base
w, feature = sess.run([weights_tensor, opt_operations[0]],feed_dict={ori_input: images_tmp2, adv_input: images_tmp2, label_ph: labels})
weight_np = weight_np + w[:FLAGS.batch_size]
# normalize the weights
weight_np = -normalize(weight_np, 2)
images_base = np.zeros_like(images)
images_base = image_preprocessing_fn(images_base)
feature_base = sess.run([opt_operations[0]],
feed_dict={ori_input: images_base, adv_input: images_base,label_ph: labels})
feature_base = feature_base[0][:FLAGS.batch_size]
# optimization
images_adv, grad_np, amplification_np=sess.run([adv_input_update, noise, amplification_update],
feed_dict={ori_input:images_tmp,adv_input:images_adv,weights_ph:weight_np, base_feature: feature_base,
label_ph:labels,accumulated_grad_ph:grad_np,amplification_ph:amplification_np})
images_adv = np.clip(images_adv, images_tmp - eps, images_tmp + eps)
images_adv = inv_image_preprocessing_fn(images_adv)
utils.save_image(images_adv, names, FLAGS.output_dir)
if __name__ == '__main__':
tf.app.run()