1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
# 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.
"""
CW-Attack test.
"""
import numpy as np
import pytest
import mindspore.ops.operations as M
from mindspore.nn import Cell
from mindspore import context
from mindarmour.adv_robustness.attacks import CarliniWagnerL2Attack
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):
"""
Introduce the layers used for network construction.
"""
super(Net, self).__init__()
self._softmax = M.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_cw_attack():
"""
CW-Attack test
"""
net = Net()
input_np = np.array([[0.1, 0.2, 0.7, 0.5, 0.4]]).astype(np.float32)
label_np = np.array([3]).astype(np.int64)
num_classes = input_np.shape[1]
attack = CarliniWagnerL2Attack(net, num_classes, targeted=False)
adv_data = attack.generate(input_np, label_np)
assert np.any(input_np != adv_data)
@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_cw_attack_targeted():
"""
CW-Attack test
"""
net = Net()
input_np = np.array([[0.1, 0.2, 0.7, 0.5, 0.4]]).astype(np.float32)
target_np = np.array([1]).astype(np.int64)
num_classes = input_np.shape[1]
attack = CarliniWagnerL2Attack(net, num_classes, targeted=True)
adv_data = attack.generate(input_np, target_np)
assert np.any(input_np != adv_data)