mnist_tutorial_deepfool.py

# Copyright 2017 - 2018 Baidu Inc.
#
# 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.
"""
DeepFool tutorial on mnist using advbox tool.
Deepfool is a simple and accurate adversarial attack method.
It supports both targeted attack and non-targeted attack.
"""
import sys
sys.path.append("..")

import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle

from advbox.adversary import Adversary
from advbox.attacks.deepfool import DeepFoolAttack
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model


def main():
    """
    Advbox demo which demonstrate how to use advbox.
    """
    TOTAL_NUM = 500
    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 = mnist_cnn_model(img)
    cost = fluid.layers.cross_entropy(input=logits, label=label)
    avg_cost = fluid.layers.mean(x=cost)

    # use CPU
    place = fluid.CPUPlace()
    # use GPU
    # place = fluid.CUDAPlace(0)
    exe = fluid.Executor(place)

    BATCH_SIZE = 1
    train_reader = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.mnist.train(), buf_size=128 * 10),
        batch_size=BATCH_SIZE)

    test_reader = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.mnist.test(), buf_size=128 * 10),
        batch_size=BATCH_SIZE)

    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),
        channel_axis=1)
    attack = DeepFoolAttack(m)
    attack_config = {"iterations": 100, "overshoot": 9}

    # use train data to generate adversarial examples
    total_count = 0
    fooling_count = 0
    for data in train_reader():
        total_count += 1
        adversary = Adversary(data[0][0], data[0][1])

        # DeepFool non-targeted attack
        adversary = attack(adversary, **attack_config)

        # DeepFool targeted attack
        # tlabel = 0
        # adversary.set_target(is_targeted_attack=True, target_label=tlabel)
        # adversary = attack(adversary, **attack_config)

        if adversary.is_successful():
            fooling_count += 1
            print(
                'attack success, original_label=%d, adversarial_label=%d, count=%d'
                % (data[0][1], adversary.adversarial_label, total_count))
            # plt.imshow(adversary.target, cmap='Greys_r')
            # plt.show()
            # np.save('adv_img', adversary.target)
        else:
            print('attack failed, original_label=%d, count=%d' %
                  (data[0][1], total_count))

        if total_count >= TOTAL_NUM:
            print(
                "[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
                % (fooling_count, total_count,
                   float(fooling_count) / total_count))
            break

    # use test data to generate adversarial examples
    total_count = 0
    fooling_count = 0
    for data in test_reader():
        total_count += 1
        adversary = Adversary(data[0][0], data[0][1])

        # DeepFool non-targeted attack
        adversary = attack(adversary, **attack_config)

        # DeepFool targeted attack
        # tlabel = 0
        # adversary.set_target(is_targeted_attack=True, target_label=tlabel)
        # adversary = attack(adversary, **attack_config)

        if adversary.is_successful():
            fooling_count += 1
            print(
                'attack success, original_label=%d, adversarial_label=%d, count=%d'
                % (data[0][1], adversary.adversarial_label, total_count))
            # plt.imshow(adversary.target, cmap='Greys_r')
            # plt.show()
            # np.save('adv_img', adversary.target)
        else:
            print('attack failed, original_label=%d, count=%d' %
                  (data[0][1], total_count))

        if total_count >= TOTAL_NUM:
            print(
                "[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
                % (fooling_count, total_count,
                   float(fooling_count) / total_count))
            break
    print("deelfool attack done")


if __name__ == '__main__':
    main()