Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into enhance-lookup_table_op-padidx
guosheng
7 years ago
commit
4c7cb771aa
@ -1,9 +0,0 @@
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# Advbox
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Advbox is a Python toolbox to create adversarial examples that fool neural networks. It requires Python and paddle.
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## How to use
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1. train a model and save it's parameters. (like fluid_mnist.py)
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2. load the parameters which is trained in step1, then reconstruct the model.(like mnist_tutorial_fgsm.py)
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3. use advbox to generate the adversarial sample.
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@ -1,16 +0,0 @@
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# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
|
||||
#
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||||
# http://www.apache.org/licenses/LICENSE-2.0
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#
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# 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.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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A set of tools for generating adversarial example on paddle platform
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"""
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@ -1,52 +0,0 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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||||
#Licensed under the Apache License, Version 2.0 (the "License");
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||||
#you may not use this file except in compliance with the License.
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#You may obtain a copy of the License at
|
||||
#
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||||
# http://www.apache.org/licenses/LICENSE-2.0
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||||
#
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||||
#Unless required by applicable law or agreed to in writing, software
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||||
#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.
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||||
"""
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The base model of the model.
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"""
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from abc import ABCMeta, abstractmethod
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class Attack(object):
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"""
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Abstract base class for adversarial attacks. `Attack` represent an adversarial attack
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which search an adversarial example. subclass should implement the _apply() method.
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Args:
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model(Model): an instance of the class advbox.base.Model.
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"""
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__metaclass__ = ABCMeta
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def __init__(self, model):
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self.model = model
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def __call__(self, image_label):
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"""
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Generate the adversarial sample.
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Args:
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image_label(list): The image and label tuple list with one element.
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"""
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adv_img = self._apply(image_label)
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return adv_img
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@abstractmethod
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def _apply(self, image_label):
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"""
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Search an adversarial example.
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Args:
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image_batch(list): The image and label tuple list with one element.
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"""
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raise NotImplementedError
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@ -1,87 +0,0 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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#Licensed under the Apache License, Version 2.0 (the "License");
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#you may not use this file except in compliance with the License.
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#You may obtain a copy of the License at
|
||||
#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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#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.
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||||
#See the License for the specific language governing permissions and
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#limitations under the License.
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"""
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This module provide the attack method for FGSM's implement.
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"""
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from __future__ import division
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import numpy as np
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from collections import Iterable
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from .base import Attack
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class GradientSignAttack(Attack):
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"""
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This attack was originally implemented by Goodfellow et al. (2015) with the
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infinity norm (and is known as the "Fast Gradient Sign Method"). This is therefore called
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the Fast Gradient Method.
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Paper link: https://arxiv.org/abs/1412.6572
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"""
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def _apply(self, image_label, epsilons=1000):
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assert len(image_label) == 1
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pre_label = np.argmax(self.model.predict(image_label))
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min_, max_ = self.model.bounds()
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gradient = self.model.gradient(image_label)
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gradient_sign = np.sign(gradient) * (max_ - min_)
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if not isinstance(epsilons, Iterable):
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epsilons = np.linspace(0, 1, num=epsilons + 1)
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for epsilon in epsilons:
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adv_img = image_label[0][0].reshape(
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gradient_sign.shape) + epsilon * gradient_sign
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adv_img = np.clip(adv_img, min_, max_)
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adv_label = np.argmax(self.model.predict([(adv_img, 0)]))
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if pre_label != adv_label:
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return adv_img
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FGSM = GradientSignAttack
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class IteratorGradientSignAttack(Attack):
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"""
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This attack was originally implemented by Alexey Kurakin(Google Brain).
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Paper link: https://arxiv.org/pdf/1607.02533.pdf
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"""
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def _apply(self, image_label, epsilons=100, steps=10):
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"""
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Apply the iterative gradient sign attack.
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Args:
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image_label(list): The image and label tuple list of one element.
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epsilons(list|tuple|int): The epsilon (input variation parameter).
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steps(int): The number of iterator steps.
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Return:
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numpy.ndarray: The adversarail sample generated by the algorithm.
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"""
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assert len(image_label) == 1
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pre_label = np.argmax(self.model.predict(image_label))
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gradient = self.model.gradient(image_label)
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min_, max_ = self.model.bounds()
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if not isinstance(epsilons, Iterable):
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epsilons = np.linspace(0, 1, num=epsilons + 1)
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for epsilon in epsilons:
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adv_img = image_label[0][0].reshape(gradient.shape)
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for _ in range(steps):
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gradient = self.model.gradient([(adv_img, image_label[0][1])])
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gradient_sign = np.sign(gradient) * (max_ - min_)
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adv_img = adv_img + epsilon * gradient_sign
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adv_img = np.clip(adv_img, min_, max_)
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adv_label = np.argmax(self.model.predict([(adv_img, 0)]))
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if pre_label != adv_label:
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return adv_img
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@ -1,16 +0,0 @@
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# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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||||
# 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.
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"""
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Paddle model for target of attack
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"""
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@ -1,103 +0,0 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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||||
#
|
||||
#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.
|
||||
"""
|
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The base model of the model.
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||||
"""
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from abc import ABCMeta
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import abc
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abstractmethod = abc.abstractmethod
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class Model(object):
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"""
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Base class of model to provide attack.
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Args:
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bounds(tuple): The lower and upper bound for the image pixel.
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channel_axis(int): The index of the axis that represents the color channel.
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preprocess(tuple): Two element tuple used to preprocess the input. First
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substract the first element, then divide the second element.
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"""
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__metaclass__ = ABCMeta
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def __init__(self, bounds, channel_axis, preprocess=None):
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assert len(bounds) == 2
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assert channel_axis in [0, 1, 2, 3]
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if preprocess is None:
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preprocess = (0, 1)
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self._bounds = bounds
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self._channel_axis = channel_axis
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self._preprocess = preprocess
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def bounds(self):
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"""
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Return the upper and lower bounds of the model.
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"""
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return self._bounds
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def channel_axis(self):
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"""
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Return the channel axis of the model.
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"""
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return self._channel_axis
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def _process_input(self, input_):
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res = input_
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sub, div = self._preprocess
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if sub != 0:
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res = input_ - sub
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assert div != 0
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if div != 1:
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res /= div
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return res
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@abstractmethod
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def predict(self, image_batch):
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"""
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Calculate the prediction of the image batch.
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Args:
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image_batch(numpy.ndarray): image batch of shape (batch_size, height, width, channels).
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Return:
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numpy.ndarray: predictions of the images with shape (batch_size, num_of_classes).
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"""
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raise NotImplementedError
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@abstractmethod
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def num_classes(self):
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"""
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Determine the number of the classes
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Return:
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int: the number of the classes
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"""
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raise NotImplementedError
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@abstractmethod
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def gradient(self, image_batch):
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"""
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Calculate the gradient of the cross-entropy loss w.r.t the image.
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Args:
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image_batch(list): The image and label tuple list.
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|
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Return:
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numpy.ndarray: gradient of the cross-entropy loss w.r.t the image with
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the shape (height, width, channel).
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"""
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raise NotImplementedError
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@ -1,114 +0,0 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
#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.
|
||||
from __future__ import absolute_import
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||||
|
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import numpy as np
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import paddle.v2 as paddle
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import paddle.v2.fluid as fluid
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from paddle.v2.fluid.framework import program_guard
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from .base import Model
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class PaddleModel(Model):
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"""
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Create a PaddleModel instance.
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When you need to generate a adversarial sample, you should construct an instance of PaddleModel.
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Args:
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program(paddle.v2.fluid.framework.Program): The program of the model which generate the adversarial sample.
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input_name(string): The name of the input.
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logits_name(string): The name of the logits.
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predict_name(string): The name of the predict.
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cost_name(string): The name of the loss in the program.
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"""
|
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|
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def __init__(self,
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program,
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input_name,
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logits_name,
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predict_name,
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cost_name,
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bounds,
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channel_axis=3,
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preprocess=None):
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super(PaddleModel, self).__init__(
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bounds=bounds, channel_axis=channel_axis, preprocess=preprocess)
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|
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if preprocess is None:
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preprocess = (0, 1)
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self._program = program
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self._place = fluid.CPUPlace()
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self._exe = fluid.Executor(self._place)
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|
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self._input_name = input_name
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self._logits_name = logits_name
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self._predict_name = predict_name
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self._cost_name = cost_name
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|
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# gradient
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loss = self._program.block(0).var(self._cost_name)
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param_grads = fluid.backward.append_backward(
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loss, parameter_list=[self._input_name])
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self._gradient = dict(param_grads)[self._input_name]
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|
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def predict(self, image_batch):
|
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"""
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Predict the label of the image_batch.
|
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|
||||
Args:
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image_batch(list): The image and label tuple list.
|
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Return:
|
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numpy.ndarray: predictions of the images with shape (batch_size, num_of_classes).
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"""
|
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feeder = fluid.DataFeeder(
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feed_list=[self._input_name, self._logits_name],
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place=self._place,
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program=self._program)
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predict_var = self._program.block(0).var(self._predict_name)
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predict = self._exe.run(self._program,
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feed=feeder.feed(image_batch),
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fetch_list=[predict_var])
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return predict
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|
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def num_classes(self):
|
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"""
|
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Calculate the number of classes of the output label.
|
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|
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Return:
|
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int: the number of classes
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"""
|
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predict_var = self._program.block(0).var(self._predict_name)
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assert len(predict_var.shape) == 2
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return predict_var.shape[1]
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|
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def gradient(self, image_batch):
|
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"""
|
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Calculate the gradient of the loss w.r.t the input.
|
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|
||||
Args:
|
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image_batch(list): The image and label tuple list.
|
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Return:
|
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list: The list of the gradient of the image.
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"""
|
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feeder = fluid.DataFeeder(
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feed_list=[self._input_name, self._logits_name],
|
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place=self._place,
|
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program=self._program)
|
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|
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grad, = self._exe.run(self._program,
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feed=feeder.feed(image_batch),
|
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fetch_list=[self._gradient])
|
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return grad
|
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@ -1,99 +0,0 @@
|
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
#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.
|
||||
"""
|
||||
CNN on mnist data using fluid api of paddlepaddle
|
||||
"""
|
||||
import paddle.v2 as paddle
|
||||
import paddle.v2.fluid as fluid
|
||||
|
||||
|
||||
def mnist_cnn_model(img):
|
||||
"""
|
||||
Mnist cnn model
|
||||
|
||||
Args:
|
||||
img(Varaible): the input image to be recognized
|
||||
|
||||
Returns:
|
||||
Variable: the label prediction
|
||||
"""
|
||||
conv_pool_1 = fluid.nets.simple_img_conv_pool(
|
||||
input=img,
|
||||
num_filters=20,
|
||||
filter_size=5,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act='relu')
|
||||
|
||||
conv_pool_2 = fluid.nets.simple_img_conv_pool(
|
||||
input=conv_pool_1,
|
||||
num_filters=50,
|
||||
filter_size=5,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act='relu')
|
||||
|
||||
logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
|
||||
return logits
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Train the cnn model on mnist datasets
|
||||
"""
|
||||
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
|
||||
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
||||
logits = mnist_cnn_model(img)
|
||||
cost = fluid.layers.cross_entropy(input=logits, label=label)
|
||||
avg_cost = fluid.layers.mean(x=cost)
|
||||
optimizer = fluid.optimizer.Adam(learning_rate=0.01)
|
||||
optimizer.minimize(avg_cost)
|
||||
|
||||
accuracy = fluid.evaluator.Accuracy(input=logits, label=label)
|
||||
|
||||
BATCH_SIZE = 50
|
||||
PASS_NUM = 3
|
||||
ACC_THRESHOLD = 0.98
|
||||
LOSS_THRESHOLD = 10.0
|
||||
train_reader = paddle.batch(
|
||||
paddle.reader.shuffle(
|
||||
paddle.dataset.mnist.train(), buf_size=500),
|
||||
batch_size=BATCH_SIZE)
|
||||
|
||||
place = fluid.CPUPlace()
|
||||
exe = fluid.Executor(place)
|
||||
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
|
||||
exe.run(fluid.default_startup_program())
|
||||
|
||||
for pass_id in range(PASS_NUM):
|
||||
accuracy.reset(exe)
|
||||
for data in train_reader():
|
||||
loss, acc = exe.run(fluid.default_main_program(),
|
||||
feed=feeder.feed(data),
|
||||
fetch_list=[avg_cost] + accuracy.metrics)
|
||||
pass_acc = accuracy.eval(exe)
|
||||
print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc="
|
||||
+ str(pass_acc))
|
||||
if loss < LOSS_THRESHOLD and pass_acc > ACC_THRESHOLD:
|
||||
break
|
||||
|
||||
pass_acc = accuracy.eval(exe)
|
||||
print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
|
||||
fluid.io.save_params(
|
||||
exe, dirname='./mnist', main_program=fluid.default_main_program())
|
||||
print('train mnist done')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,100 +0,0 @@
|
||||
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
#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.
|
||||
"""
|
||||
FGSM demos on mnist using advbox tool.
|
||||
"""
|
||||
import paddle.v2 as paddle
|
||||
import paddle.v2.fluid as fluid
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
from advbox.models.paddle import PaddleModel
|
||||
from advbox.attacks.gradientsign import GradientSignAttack
|
||||
|
||||
|
||||
def cnn_model(img):
|
||||
"""
|
||||
Mnist cnn model
|
||||
Args:
|
||||
img(Varaible): the input image to be recognized
|
||||
Returns:
|
||||
Variable: the label prediction
|
||||
"""
|
||||
#conv1 = fluid.nets.conv2d()
|
||||
conv_pool_1 = fluid.nets.simple_img_conv_pool(
|
||||
input=img,
|
||||
num_filters=20,
|
||||
filter_size=5,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act='relu')
|
||||
|
||||
conv_pool_2 = fluid.nets.simple_img_conv_pool(
|
||||
input=conv_pool_1,
|
||||
num_filters=50,
|
||||
filter_size=5,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act='relu')
|
||||
|
||||
logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
|
||||
return logits
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Advbox demo which demonstrate how to use advbox.
|
||||
"""
|
||||
IMG_NAME = 'img'
|
||||
LABEL_NAME = 'label'
|
||||
|
||||
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
|
||||
# gradient should flow
|
||||
img.stop_gradient = False
|
||||
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
|
||||
logits = cnn_model(img)
|
||||
cost = fluid.layers.cross_entropy(input=logits, label=label)
|
||||
avg_cost = fluid.layers.mean(x=cost)
|
||||
|
||||
place = fluid.CPUPlace()
|
||||
exe = fluid.Executor(place)
|
||||
|
||||
BATCH_SIZE = 1
|
||||
train_reader = paddle.batch(
|
||||
paddle.reader.shuffle(
|
||||
paddle.dataset.mnist.train(), buf_size=500),
|
||||
batch_size=BATCH_SIZE)
|
||||
feeder = fluid.DataFeeder(
|
||||
feed_list=[IMG_NAME, LABEL_NAME],
|
||||
place=place,
|
||||
program=fluid.default_main_program())
|
||||
|
||||
fluid.io.load_params(
|
||||
exe, "./mnist/", main_program=fluid.default_main_program())
|
||||
|
||||
# advbox demo
|
||||
m = PaddleModel(fluid.default_main_program(), IMG_NAME, LABEL_NAME,
|
||||
logits.name, avg_cost.name, (-1, 1))
|
||||
att = GradientSignAttack(m)
|
||||
for data in train_reader():
|
||||
# fgsm attack
|
||||
adv_img = att(data)
|
||||
plt.imshow(n[0][0], cmap='Greys_r')
|
||||
plt.show()
|
||||
#np.save('adv_img', adv_img)
|
||||
break
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
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Reference in new issue