mnist_attack_deepfool.py

# Copyright 2019 Huawei Technologies Co., Ltd
#
# 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.
import sys
import time
import numpy as np
import pytest
from scipy.special import softmax

from mindspore import Model
from mindspore import Tensor
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net

from mindarmour.attacks.deep_fool import DeepFool
from mindarmour.utils.logger import LogUtil
from mindarmour.evaluations.attack_evaluation import AttackEvaluate

from lenet5_net import LeNet5

context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")


sys.path.append("..")
from data_processing import generate_mnist_dataset

LOGGER = LogUtil.get_instance()
TAG = 'DeepFool_Test'


@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_card
@pytest.mark.component_mindarmour
def test_deepfool_attack():
    """
    DeepFool-Attack test
    """
    # upload trained network
    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
    net = LeNet5()
    load_dict = load_checkpoint(ckpt_name)
    load_param_into_net(net, load_dict)

    # get test data
    data_list = "./MNIST_unzip/test"
    batch_size = 32
    ds = generate_mnist_dataset(data_list, batch_size=batch_size)

    # prediction accuracy before attack
    model = Model(net)
    batch_num = 3  # the number of batches of attacking samples
    test_images = []
    test_labels = []
    predict_labels = []
    i = 0
    for data in ds.create_tuple_iterator():
        i += 1
        images = data[0].astype(np.float32)
        labels = data[1]
        test_images.append(images)
        test_labels.append(labels)
        pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),
                                axis=1)
        predict_labels.append(pred_labels)
        if i >= batch_num:
            break
    predict_labels = np.concatenate(predict_labels)
    true_labels = np.concatenate(test_labels)
    accuracy = np.mean(np.equal(predict_labels, true_labels))
    LOGGER.info(TAG, "prediction accuracy before attacking is : %s", accuracy)

    # attacking
    classes = 10
    attack = DeepFool(net, classes, norm_level=2,
                      bounds=(0.0, 1.0))
    start_time = time.clock()
    adv_data = attack.batch_generate(np.concatenate(test_images),
                                     np.concatenate(test_labels), batch_size=32)
    stop_time = time.clock()
    pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()
    # rescale predict confidences into (0, 1).
    pred_logits_adv = softmax(pred_logits_adv, axis=1)
    pred_labels_adv = np.argmax(pred_logits_adv, axis=1)
    accuracy_adv = np.mean(np.equal(pred_labels_adv, true_labels))
    LOGGER.info(TAG, "prediction accuracy after attacking is : %s",
                accuracy_adv)
    test_labels = np.eye(10)[np.concatenate(test_labels)]
    attack_evaluate = AttackEvaluate(np.concatenate(test_images).transpose(0, 2, 3, 1),
                                     test_labels, adv_data.transpose(0, 2, 3, 1),
                                     pred_logits_adv)
    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
                attack_evaluate.mis_classification_rate())
    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
                attack_evaluate.avg_conf_adv_class())
    LOGGER.info(TAG, 'The average confidence of true class is : %s',
                attack_evaluate.avg_conf_true_class())
    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
                     'samples and adversarial samples are: %s',
                attack_evaluate.avg_lp_distance())
    LOGGER.info(TAG, 'The average structural similarity between original '
                     'samples and adversarial samples are: %s',
                attack_evaluate.avg_ssim())
    LOGGER.info(TAG, 'The average costing time is %s',
                (stop_time - start_time)/(batch_num*batch_size))


if __name__ == '__main__':
    test_deepfool_attack()