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serving_client.py 2.0 kB

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  1. # Copyright 2021 Huawei Technologies Co., Ltd
  2. #
  3. # Licensed under the Apache License, Version 2.0 (the "License");
  4. # you may not use this file except in compliance with the License.
  5. # You may obtain a copy of the License at
  6. #
  7. # http://www.apache.org/licenses/LICENSE-2.0
  8. #
  9. # Unless required by applicable law or agreed to in writing, software
  10. # distributed under the License is distributed on an "AS IS" BASIS,
  11. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12. # See the License for the specific language governing permissions and
  13. # limitations under the License.
  14. # ============================================================================
  15. """The client of example add."""
  16. import os
  17. import json
  18. from io import BytesIO
  19. import cv2
  20. from PIL import Image
  21. from mindspore_serving.client import Client
  22. from perturb_config import PerturbConfig
  23. def perturb(perturb_config):
  24. """Invoke servable perturbation method natural_perturbation"""
  25. client = Client("10.175.244.87:5500", "perturbation", "natural_perturbation")
  26. instances = []
  27. img_path = '/root/mindarmour/example/adversarial/test_data/1.png'
  28. result_path = '/root/mindarmour/example/adv/result/'
  29. methods_number = 2
  30. outputs_number = 3
  31. img = cv2.imread(img_path)
  32. img = cv2.imencode('.png', img)[1].tobytes()
  33. perturb_config = json.dumps(perturb_config)
  34. instances.append({"img": img, 'perturb_config': perturb_config, "methods_number": methods_number,
  35. "outputs_number": outputs_number})
  36. result = client.infer(instances)
  37. file_names = result[0]['file_names'].split(';')
  38. length = result[0]['file_length'].tolist()
  39. before = 0
  40. for name, leng in zip(file_names, length):
  41. res_img = result[0]['results']
  42. res_img = res_img[before:before + leng]
  43. before = before + leng
  44. print('name: ', name)
  45. image = Image.open(BytesIO(res_img))
  46. image.save(os.path.join(result_path, name))
  47. if __name__ == '__main__':
  48. perturb(PerturbConfig)

MindArmour关注AI的安全和隐私问题。致力于增强模型的安全可信、保护用户的数据隐私。主要包含3个模块:对抗样本鲁棒性模块、Fuzz Testing模块、隐私保护与评估模块。 对抗样本鲁棒性模块 对抗样本鲁棒性模块用于评估模型对于对抗样本的鲁棒性,并提供模型增强方法用于增强模型抗对抗样本攻击的能力,提升模型鲁棒性。对抗样本鲁棒性模块包含了4个子模块:对抗样本的生成、对抗样本的检测、模型防御、攻防评估。