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101 lines
2.8 KiB
101 lines
2.8 KiB
# 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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#
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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,
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#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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FGSM demos on mnist using advbox tool.
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"""
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import paddle.v2 as paddle
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import paddle.v2.fluid as fluid
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import matplotlib.pyplot as plt
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import numpy as np
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from advbox.models.paddle import PaddleModel
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from advbox.attacks.gradientsign import GradientSignAttack
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def cnn_model(img):
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"""
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Mnist cnn model
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Args:
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img(Varaible): the input image to be recognized
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Returns:
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Variable: the label prediction
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"""
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#conv1 = fluid.nets.conv2d()
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conv_pool_1 = fluid.nets.simple_img_conv_pool(
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input=img,
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num_filters=20,
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filter_size=5,
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pool_size=2,
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pool_stride=2,
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act='relu')
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conv_pool_2 = fluid.nets.simple_img_conv_pool(
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input=conv_pool_1,
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num_filters=50,
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filter_size=5,
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pool_size=2,
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pool_stride=2,
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act='relu')
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logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
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return logits
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def main():
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"""
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Advbox demo which demonstrate how to use advbox.
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"""
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IMG_NAME = 'img'
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LABEL_NAME = 'label'
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img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
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# gradient should flow
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img.stop_gradient = False
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label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
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logits = cnn_model(img)
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cost = fluid.layers.cross_entropy(input=logits, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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BATCH_SIZE = 1
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.mnist.train(), buf_size=500),
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batch_size=BATCH_SIZE)
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feeder = fluid.DataFeeder(
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feed_list=[IMG_NAME, LABEL_NAME],
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place=place,
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program=fluid.default_main_program())
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fluid.io.load_params(
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exe, "./mnist/", main_program=fluid.default_main_program())
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# advbox demo
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m = PaddleModel(fluid.default_main_program(), IMG_NAME, LABEL_NAME,
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logits.name, avg_cost.name, (-1, 1))
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att = GradientSignAttack(m)
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for data in train_reader():
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# fgsm attack
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adv_img = att(data)
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plt.imshow(n[0][0], cmap='Greys_r')
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plt.show()
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#np.save('adv_img', adv_img)
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break
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if __name__ == '__main__':
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main()
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