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# Copyright 2018 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Library with adversarial attacks.
This library designed to be self-contained and have no dependencies other
than TensorFlow. It only contains PGD / Iterative FGSM attacks,
see https://arxiv.org/abs/1706.06083 and https://arxiv.org/abs/1607.02533
for details.
For wider set of adversarial attacks refer to Cleverhans library:
https://github.com/tensorflow/cleverhans
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
def generate_pgd_common(x,
bounds,
model_fn,
attack_params,
one_hot_labels,
perturbation_multiplier):
"""Common code for generating PGD adversarial examples.
Args:
x: original examples.
bounds: tuple with bounds of image values, bounds[0] < bounds[1].
model_fn: model function with signature model_fn(images).
attack_params: parameters of the attack.
one_hot_labels: one hot label vector to use in the loss.
perturbation_multiplier: multiplier of adversarial perturbation,
either +1.0 or -1.0.
Returns:
Tensor with adversarial examples.
Raises:
ValueError: if attack parameters are invalid.
"""
# parse attack_params
# Format of attack_params: 'EPS_STEP_NITER'
# where EPS - epsilon, STEP - step size, NITER - number of iterations
params_list = attack_params.split('_')
if len(params_list) != 3:
raise ValueError('Invalid parameters of PGD attack: %s' % attack_params)
epsilon = int(params_list[0])
step_size = int(params_list[1])
niter = int(params_list[2])
# rescale epsilon and step size to image bounds
epsilon = float(epsilon) / 255.0 * (bounds[1] - bounds[0])
step_size = float(step_size) / 255.0 * (bounds[1] - bounds[0])
# clipping boundaries
clip_min = tf.maximum(x - epsilon, bounds[0])
clip_max = tf.minimum(x + epsilon, bounds[1])
# compute starting point
start_x = x + tf.random_uniform(tf.shape(x), -epsilon, epsilon)
start_x = tf.clip_by_value(start_x, clip_min, clip_max)
# main iteration of PGD
loop_vars = [0, start_x]
def loop_cond(index, _):
return index < niter
def loop_body(index, adv_images):
logits = model_fn(adv_images)
loss = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits_v2(
labels=one_hot_labels,
logits=logits))
perturbation = step_size * tf.sign(tf.gradients(loss, adv_images)[0])
new_adv_images = adv_images + perturbation_multiplier * perturbation
new_adv_images = tf.clip_by_value(new_adv_images, clip_min, clip_max)
return index + 1, new_adv_images
with tf.control_dependencies([start_x]):
_, result = tf.while_loop(
loop_cond,
loop_body,
loop_vars,
back_prop=False,
parallel_iterations=1)
return result
def generate_pgd_ll(x, bounds, model_fn, attack_params):
# pylint: disable=g-doc-args
"""Generats targeted PGD adversarial examples with least likely target class.
See generate_pgd_common for description of arguments.
Returns:
Tensor with adversarial examples.
"""
# pylint: enable=g-doc-args
# compute one hot least likely class
logits = model_fn(x)
num_classes = tf.shape(logits)[1]
one_hot_labels = tf.one_hot(tf.argmin(model_fn(x), axis=1), num_classes)
return generate_pgd_common(x, bounds, model_fn, attack_params,
one_hot_labels=one_hot_labels,
perturbation_multiplier=-1.0)
def generate_pgd_rand(x, bounds, model_fn, attack_params):
# pylint: disable=g-doc-args
"""Generats targeted PGD adversarial examples with random target class.
See generate_pgd_common for description of arguments.
Returns:
Tensor with adversarial examples.
"""
# pylint: enable=g-doc-args
# compute one hot random class
logits = model_fn(x)
batch_size = tf.shape(logits)[0]
num_classes = tf.shape(logits)[1]
random_labels = tf.random_uniform(shape=[batch_size],
minval=0,
maxval=num_classes,
dtype=tf.int32)
one_hot_labels = tf.one_hot(random_labels, num_classes)
return generate_pgd_common(x, bounds, model_fn, attack_params,
one_hot_labels=one_hot_labels,
perturbation_multiplier=-1.0)
def generate_pgd(x, bounds, model_fn, attack_params):
# pylint: disable=g-doc-args
"""Generats non-targeted PGD adversarial examples.
See generate_pgd_common for description of arguments.
Returns:
tensor with adversarial examples.
"""
# pylint: enable=g-doc-args
# compute one hot predicted class
logits = model_fn(x)
num_classes = tf.shape(logits)[1]
one_hot_labels = tf.one_hot(tf.argmax(model_fn(x), axis=1), num_classes)
return generate_pgd_common(x, bounds, model_fn, attack_params,
one_hot_labels=one_hot_labels,
perturbation_multiplier=1.0)
def generate_adversarial_examples(x, bounds, model_fn, attack_description):
"""Generates adversarial examples.
Args:
x: original examples.
bounds: tuple with bounds of image values, bounds[0] < bounds[1]
model_fn: model function with signature model_fn(images).
attack_description: string which describes an attack, see notes below for
details.
Returns:
Tensor with adversarial examples.
Raises:
ValueError: if attack description is invalid.
Attack description could be one of the following strings:
- "clean" - no attack, return original images.
- "pgd_EPS_STEP_NITER" - non-targeted PGD attack.
- "pgdll_EPS_STEP_NITER" - tageted PGD attack with least likely target class.
- "pgdrnd_EPS_STEP_NITER" - targetd PGD attack with random target class.
Meaning of attack parameters is following:
- EPS - maximum size of adversarial perturbation, between 0 and 255.
- STEP - step size of one iteration of PGD, between 0 and 255.
- NITER - number of iterations.
"""
if attack_description == 'clean':
return x
idx = attack_description.find('_')
if idx < 0:
raise ValueError('Invalid value of attack description %s'
% attack_description)
attack_name = attack_description[:idx]
attack_params = attack_description[idx+1:]
if attack_name == 'pgdll':
return generate_pgd_ll(x, bounds, model_fn, attack_params)
elif attack_name == 'pgdrnd':
return generate_pgd_rand(x, bounds, model_fn, attack_params)
elif attack_name == 'pgd':
return generate_pgd(x, bounds, model_fn, attack_params)
else:
raise ValueError('Invalid value of attack description %s'
% attack_description)
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