mindarmour/tests/ut/python/attacks/test_iterative_gradient_method.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.
"""
Iterative-gradient Attack test.
"""
import numpy as np
import pytest

from mindspore.ops import operations as P
from mindspore.nn import Cell
from mindspore import context

from mindarmour.adv_robustness.attacks import BasicIterativeMethod
from mindarmour.adv_robustness.attacks import MomentumIterativeMethod
from mindarmour.adv_robustness.attacks import ProjectedGradientDescent
from mindarmour.adv_robustness.attacks import IterativeGradientMethod
from mindarmour.adv_robustness.attacks import DiverseInputIterativeMethod
from mindarmour.adv_robustness.attacks import MomentumDiverseInputIterativeMethod

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


# for user
class Net(Cell):
    """
    Construct the network of target model.

    Examples:
        >>> net = Net()
    """

    def __init__(self):
        super(Net, self).__init__()
        self._softmax = P.Softmax()

    def construct(self, inputs):
        """
        Construct network.

        Args:
            inputs (Tensor): Input data.
        """
        out = self._softmax(inputs)
        return out


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_card
@pytest.mark.component_mindarmour
def test_basic_iterative_method():
    """
    Basic iterative method unit test.
    """
    input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
    label = np.asarray([2], np.int32)
    label = np.eye(3)[label].astype(np.float32)

    for i in range(5):
        net = Net()
        attack = BasicIterativeMethod(net, nb_iter=i + 1)
        ms_adv_x = attack.generate(input_np, label)
        assert np.any(
            ms_adv_x != input_np), 'Basic iterative method: generate value' \
                                   ' must not be equal to original value.'


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_card
@pytest.mark.component_mindarmour
def test_momentum_iterative_method():
    """
    Momentum iterative method unit test.
    """
    input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
    label = np.asarray([2], np.int32)
    label = np.eye(3)[label].astype(np.float32)

    for i in range(5):
        attack = MomentumIterativeMethod(Net(), nb_iter=i + 1)
        ms_adv_x = attack.generate(input_np, label)
        assert np.any(ms_adv_x != input_np), 'Momentum iterative method: generate' \
                                             ' value must not be equal to' \
                                             ' original value.'


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_card
@pytest.mark.component_mindarmour
def test_projected_gradient_descent_method():
    """
    Projected gradient descent method unit test.
    """
    input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
    label = np.asarray([2], np.int32)
    label = np.eye(3)[label].astype(np.float32)

    for i in range(5):
        attack = ProjectedGradientDescent(Net(), nb_iter=i + 1)
        ms_adv_x = attack.generate(input_np, label)

        assert np.any(
            ms_adv_x != input_np), 'Projected gradient descent method: ' \
                                   'generate value must not be equal to' \
                                   ' original value.'


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_card
@pytest.mark.component_mindarmour
def test_diverse_input_iterative_method():
    """
    Diverse input iterative method unit test.
    """
    input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
    label = np.asarray([2], np.int32)
    label = np.eye(3)[label].astype(np.float32)

    attack = DiverseInputIterativeMethod(Net())
    ms_adv_x = attack.generate(input_np, label)
    assert np.any(ms_adv_x != input_np), 'Diverse input iterative method: generate' \
                                             ' value must not be equal to' \
                                             ' original value.'


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_card
@pytest.mark.component_mindarmour
def test_momentum_diverse_input_iterative_method():
    """
    Momentum diverse input iterative method unit test.
    """
    input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
    label = np.asarray([2], np.int32)
    label = np.eye(3)[label].astype(np.float32)

    attack = MomentumDiverseInputIterativeMethod(Net())
    ms_adv_x = attack.generate(input_np, label)
    assert np.any(ms_adv_x != input_np), 'Momentum diverse input iterative method: ' \
                                             'generate value must not be equal to' \
                                             ' original value.'


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_card
@pytest.mark.component_mindarmour
def test_error():
    with pytest.raises(TypeError):
        # check_param_multi_types
        assert IterativeGradientMethod(Net(), bounds=None)
    attack = IterativeGradientMethod(Net(), bounds=(0.0, 1.0))
    with pytest.raises(NotImplementedError):
        input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
        label = np.asarray([2], np.int32)
        label = np.eye(3)[label].astype(np.float32)
        assert attack.generate(input_np, label)