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- # Copyright 2022 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.
- # ============================================================================
- """non target attack"""
- import numpy as np
- import matplotlib.image as mp
- from mindspore import context
- import adversarial_attack
- from FaceRecognition.eval import get_model
-
- context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-
-
- if __name__ == '__main__':
-
- inputs = adversarial_attack.load_data('photos/input/')
- targets = adversarial_attack.load_data('photos/target/')
-
- net = get_model()
- adversarial = adversarial_attack.FaceAdversarialAttack(inputs[0], targets[0], net)
- ATTACK_METHOD = "non_target_attack"
-
- tensor_dict = adversarial.train(attack_method=ATTACK_METHOD)
-
- mp.imsave('./outputs/adversarial_example.jpg',
- np.transpose(tensor_dict.get("adversarial_tensor").asnumpy(), (1, 2, 0)))
- mp.imsave('./outputs/mask.jpg',
- np.transpose(tensor_dict.get("mask_tensor").asnumpy(), (1, 2, 0)))
- mp.imsave('./outputs/input_image.jpg',
- np.transpose(tensor_dict.get("processed_input_tensor").asnumpy(), (1, 2, 0)))
- mp.imsave('./outputs/target_image.jpg',
- np.transpose(tensor_dict.get("processed_target_tensor").asnumpy(), (1, 2, 0)))
-
- adversarial.test_non_target_attack()
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