mnist_tutorial_ilcm.py

"""
ILCM tutorial on mnist using advbox tool.
ILCM method extends "BIM" to support targeted attack.
"""
import sys
sys.path.append("..")

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

from advbox.adversary import Adversary
from advbox.attacks.gradient_method import ILCM
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 = ILCM(m)
    attack_config = {"epsilons": 0.1, "steps": 100}

    # 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])
        tlabel = 0
        adversary.set_target(is_targeted_attack=True, target_label=tlabel)

        # ILCM targeted attack
        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])
        tlabel = 0
        adversary.set_target(is_targeted_attack=True, target_label=tlabel)

        # ILCM targeted attack
        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("ilcm attack done")


if __name__ == '__main__':
    main()