adjust the project structure according to diff features

example/membership_inference_demo/main.py

@@ -28,7 +28,7 @@ import numpy as np
 from mindspore.train import Model
 from mindspore.train.serialization import load_param_into_net, load_checkpoint
 import mindspore.nn as nn
-from mindarmour.diff_privacy.evaluation.membership_inference import MembershipInference
+from mindarmour.privacy.evaluation import MembershipInference
 from mindarmour.utils import LogUtil
 logging = LogUtil.get_instance()
 logging.set_level(20)

example/mnist_demo/lenet5_dp.py

@@ -29,10 +29,10 @@ import mindspore.dataset.transforms.c_transforms as C
 from mindspore.dataset.transforms.vision import Inter
 import mindspore.common.dtype as mstype
 
-from mindarmour.diff_privacy import DPModel
-from mindarmour.diff_privacy import PrivacyMonitorFactory
-from mindarmour.diff_privacy import NoiseMechanismsFactory
-from mindarmour.diff_privacy import ClipMechanismsFactory
+from mindarmour.privacy.diff_privacy import DPModel
+from mindarmour.privacy.diff_privacy import PrivacyMonitorFactory
+from mindarmour.privacy.diff_privacy import NoiseMechanismsFactory
+from mindarmour.privacy.diff_privacy import ClipMechanismsFactory
 from mindarmour.utils.logger import LogUtil
 from lenet5_net import LeNet5
 from lenet5_config import mnist_cfg as cfg

example/mnist_demo/lenet5_dp_ada_gaussian.py

@@ -29,9 +29,9 @@ import mindspore.dataset.transforms.c_transforms as C
 from mindspore.dataset.transforms.vision import Inter
 import mindspore.common.dtype as mstype
 
-from mindarmour.diff_privacy import DPModel
-from mindarmour.diff_privacy import PrivacyMonitorFactory
-from mindarmour.diff_privacy import NoiseMechanismsFactory
+from mindarmour.privacy.diff_privacy import DPModel
+from mindarmour.privacy.diff_privacy import PrivacyMonitorFactory
+from mindarmour.privacy.diff_privacy import NoiseMechanismsFactory
 from mindarmour.utils.logger import LogUtil
 from lenet5_net import LeNet5
 from dp_ada_gaussian_config import mnist_cfg as cfg

example/mnist_demo/lenet5_dp_ada_sgd_graph.py

@@ -29,9 +29,9 @@ import mindspore.dataset.transforms.c_transforms as C
 from mindspore.dataset.transforms.vision import Inter
 import mindspore.common.dtype as mstype
 
-from mindarmour.diff_privacy import DPModel
-from mindarmour.diff_privacy import PrivacyMonitorFactory
-from mindarmour.diff_privacy import NoiseMechanismsFactory
+from mindarmour.privacy.diff_privacy import DPModel
+from mindarmour.privacy.diff_privacy import PrivacyMonitorFactory
+from mindarmour.privacy.diff_privacy import NoiseMechanismsFactory
 from mindarmour.utils.logger import LogUtil
 from lenet5_net import LeNet5
 from dp_ada_sgd_graph_config import mnist_cfg as cfg

example/mnist_demo/lenet5_dp_optimizer.py

@@ -29,10 +29,10 @@ import mindspore.dataset.transforms.c_transforms as C
 from mindspore.dataset.transforms.vision import Inter
 import mindspore.common.dtype as mstype
 
-from mindarmour.diff_privacy import DPModel
-from mindarmour.diff_privacy import PrivacyMonitorFactory
-from mindarmour.diff_privacy import DPOptimizerClassFactory
-from mindarmour.diff_privacy import ClipMechanismsFactory
+from mindarmour.privacy.diff_privacy import DPModel
+from mindarmour.privacy.diff_privacy import PrivacyMonitorFactory
+from mindarmour.privacy.diff_privacy import DPOptimizerClassFactory
+from mindarmour.privacy.diff_privacy import ClipMechanismsFactory
 from mindarmour.utils.logger import LogUtil
 from lenet5_net import LeNet5
 from lenet5_config import mnist_cfg as cfg

example/mnist_demo/lenet5_mnist_coverage.py

@@ -20,8 +20,8 @@ from mindspore.nn import SoftmaxCrossEntropyWithLogits
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.gradient_method import FastGradientSignMethod
-from mindarmour.fuzzing.model_coverage_metrics import ModelCoverageMetrics
+from mindarmour.adv_robustness.attacks import FastGradientSignMethod
+from mindarmour.fuzz_testing import ModelCoverageMetrics
 from mindarmour.utils.logger import LogUtil
 
 sys.path.append("..")

example/mnist_demo/lenet5_mnist_fuzzing.py

@@ -19,8 +19,8 @@ from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
 from lenet5_net import LeNet5
-from mindarmour.fuzzing.fuzzing import Fuzzer
-from mindarmour.fuzzing.model_coverage_metrics import ModelCoverageMetrics
+from mindarmour.fuzz_testing import Fuzzer
+from mindarmour.fuzz_testing import ModelCoverageMetrics
 from mindarmour.utils.logger import LogUtil
 
 sys.path.append("..")

example/mnist_demo/mnist_attack_cw.py

@@ -22,8 +22,8 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.carlini_wagner import CarliniWagnerL2Attack
-from mindarmour.evaluations.attack_evaluation import AttackEvaluate
+from mindarmour.adv_robustness.attacks import CarliniWagnerL2Attack
+from mindarmour.adv_robustness.evaluations import AttackEvaluate
 from mindarmour.utils.logger import LogUtil
 
 

example/mnist_demo/mnist_attack_deepfool.py

@@ -22,8 +22,8 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.deep_fool import DeepFool
-from mindarmour.evaluations.attack_evaluation import AttackEvaluate
+from mindarmour.adv_robustness.attacks.deep_fool import DeepFool
+from mindarmour.adv_robustness.evaluations import AttackEvaluate
 from mindarmour.utils.logger import LogUtil
 
 

example/mnist_demo/mnist_attack_fgsm.py

@@ -23,8 +23,8 @@ from mindspore.nn import SoftmaxCrossEntropyWithLogits
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.gradient_method import FastGradientSignMethod
-from mindarmour.evaluations.attack_evaluation import AttackEvaluate
+from mindarmour.adv_robustness.attacks import FastGradientSignMethod
+from mindarmour.adv_robustness.evaluations import AttackEvaluate
 from mindarmour.utils.logger import LogUtil
 
 

example/mnist_demo/mnist_attack_genetic.py

@@ -21,9 +21,9 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.genetic_attack import GeneticAttack
-from mindarmour.evaluations.attack_evaluation import AttackEvaluate
+from mindarmour.adv_robustness.attacks import BlackModel
+from mindarmour.adv_robustness.attacks.black.genetic_attack import GeneticAttack
+from mindarmour.adv_robustness.evaluations import AttackEvaluate
 from mindarmour.utils.logger import LogUtil
 
 

example/mnist_demo/mnist_attack_hsja.py

@@ -19,8 +19,8 @@ from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.hop_skip_jump_attack import HopSkipJumpAttack
+from mindarmour import BlackModel
+from mindarmour.adv_robustness.attacks import HopSkipJumpAttack
 from mindarmour.utils.logger import LogUtil
 
 sys.path.append("..")

example/mnist_demo/mnist_attack_jsma.py

@@ -22,8 +22,8 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.jsma import JSMAAttack
-from mindarmour.evaluations.attack_evaluation import AttackEvaluate
+from mindarmour.adv_robustness.attacks import JSMAAttack
+from mindarmour.adv_robustness.evaluations import AttackEvaluate
 from mindarmour.utils.logger import LogUtil
 
 

example/mnist_demo/mnist_attack_lbfgs.py

@@ -23,8 +23,8 @@ from mindspore.nn import SoftmaxCrossEntropyWithLogits
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.lbfgs import LBFGS
-from mindarmour.evaluations.attack_evaluation import AttackEvaluate
+from mindarmour.adv_robustness.attacks import LBFGS
+from mindarmour.adv_robustness.evaluations import AttackEvaluate
 from mindarmour.utils.logger import LogUtil
 
 

example/mnist_demo/mnist_attack_mdi2fgsm.py

@@ -23,9 +23,8 @@ from mindspore.nn import SoftmaxCrossEntropyWithLogits
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.iterative_gradient_method import \
-    MomentumDiverseInputIterativeMethod
-from mindarmour.evaluations.attack_evaluation import AttackEvaluate
+from mindarmour.adv_robustness.attacks import MomentumDiverseInputIterativeMethod
+from mindarmour.adv_robustness.evaluations import AttackEvaluate
 from mindarmour.utils.logger import LogUtil
 
 

example/mnist_demo/mnist_attack_nes.py

@@ -19,8 +19,8 @@ from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.natural_evolutionary_strategy import NES
+from mindarmour import BlackModel
+from mindarmour.adv_robustness.attacks import NES
 from mindarmour.utils.logger import LogUtil
 
 sys.path.append("..")

example/mnist_demo/mnist_attack_pgd.py

@@ -23,8 +23,8 @@ from mindspore.nn import SoftmaxCrossEntropyWithLogits
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.iterative_gradient_method import ProjectedGradientDescent
-from mindarmour.evaluations.attack_evaluation import AttackEvaluate
+from mindarmour.adv_robustness.attacks import ProjectedGradientDescent
+from mindarmour.adv_robustness.evaluations import AttackEvaluate
 from mindarmour.utils.logger import LogUtil
 
 

example/mnist_demo/mnist_attack_pointwise.py

@@ -20,9 +20,9 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.pointwise_attack import PointWiseAttack
-from mindarmour.evaluations.attack_evaluation import AttackEvaluate
+from mindarmour import BlackModel
+from mindarmour.adv_robustness.attacks import PointWiseAttack
+from mindarmour.adv_robustness.evaluations import AttackEvaluate
 from mindarmour.utils.logger import LogUtil
 
 sys.path.append("..")

example/mnist_demo/mnist_attack_pso.py

@@ -21,9 +21,9 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.pso_attack import PSOAttack
-from mindarmour.evaluations.attack_evaluation import AttackEvaluate
+from mindarmour import BlackModel
+from mindarmour.adv_robustness.attacks.black.pso_attack import PSOAttack
+from mindarmour.adv_robustness.evaluations import AttackEvaluate
 from mindarmour.utils.logger import LogUtil
 
 

example/mnist_demo/mnist_attack_salt_and_pepper.py

@@ -20,9 +20,9 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.salt_and_pepper_attack import SaltAndPepperNoiseAttack
-from mindarmour.evaluations.attack_evaluation import AttackEvaluate
+from mindarmour import BlackModel
+from mindarmour.adv_robustness.attacks import SaltAndPepperNoiseAttack
+from mindarmour.adv_robustness.evaluations import AttackEvaluate
 from mindarmour.utils.logger import LogUtil
 
 sys.path.append("..")

example/mnist_demo/mnist_defense_nad.py

@@ -23,8 +23,8 @@ from mindspore.nn import SoftmaxCrossEntropyWithLogits
 from mindspore.train import Model
 from mindspore.train.callback import LossMonitor
 
-from mindarmour.attacks import FastGradientSignMethod
-from mindarmour.defenses import NaturalAdversarialDefense
+from mindarmour.adv_robustness.attacks import FastGradientSignMethod
+from mindarmour.adv_robustness.defenses import NaturalAdversarialDefense
 from mindarmour.utils.logger import LogUtil
 
 from lenet5_net import LeNet5

example/mnist_demo/mnist_evaluation.py

@@ -28,13 +28,13 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks import FastGradientSignMethod
-from mindarmour.attacks import GeneticAttack
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.defenses import NaturalAdversarialDefense
-from mindarmour.detectors.black.similarity_detector import SimilarityDetector
-from mindarmour.evaluations import BlackDefenseEvaluate
-from mindarmour.evaluations import DefenseEvaluate
+from mindarmour.adv_robustness.attacks import FastGradientSignMethod
+from mindarmour.adv_robustness.attacks import GeneticAttack
+from mindarmour.adv_robustness.attacks import BlackModel
+from mindarmour.adv_robustness.defenses import NaturalAdversarialDefense
+from mindarmour.adv_robustness.detectors import SimilarityDetector
+from mindarmour.adv_robustness.evaluations import BlackDefenseEvaluate
+from mindarmour.adv_robustness.evaluations import DefenseEvaluate
 from mindarmour.utils.logger import LogUtil
 
 sys.path.append("..")

example/mnist_demo/mnist_similarity_detector.py

@@ -23,9 +23,9 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from scipy.special import softmax
 
 from lenet5_net import LeNet5
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.pso_attack import PSOAttack
-from mindarmour.detectors.black.similarity_detector import SimilarityDetector
+from mindarmour import BlackModel
+from mindarmour.adv_robustness.attacks.black.pso_attack import PSOAttack
+from mindarmour.adv_robustness.detectors import SimilarityDetector
 from mindarmour.utils.logger import LogUtil
 
 

mindarmour/__init__.py

 """
-MindArmour, a tool box of MindSpore to enhance model security and
-trustworthiness against adversarial examples.
+MindArmour, a tool box of MindSpore to enhance model trustworthiness and achieve
+privacy-preserving machine learning.
 """
-from .attacks import Attack
-from .attacks.black.black_model import BlackModel
-from .defenses.defense import Defense
-from .detectors.detector import Detector
+from .adv_robustness.attacks import Attack
+from .adv_robustness.attacks.black.black_model import BlackModel
+from .adv_robustness.defenses.defense import Defense
+from .adv_robustness.detectors.detector import Detector
+from .fuzz_testing.fuzzing import Fuzzer
+from .privacy.diff_privacy import DPModel
+from .privacy.evaluation.membership_inference import MembershipInference
 
 __all__ = ['Attack',
            'BlackModel',
            'Detector',
-           'Defense']
+           'Defense',
+           'Fuzzer',
+           'DPModel',
+           'MembershipInference']

mindarmour/adv_robustness/__init__.py

+# Copyright 2020 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.
+"""
+Adversarial Robustness.
+
+This module is a tool box to enhance model security and against adversarial
+examples.
+"""

mindarmour/attacks/__init__.py → mindarmour/adv_robustness/attacks/__init__.py

+# Copyright 2020 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.
 """
 This module includes classical black-box and white-box attack algorithms
 in making adversarial examples.

mindarmour/adv_robustness/attacks/black/__init__.py

+# Copyright 2020 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.
+"""
+This module is a submodule, which provides some black-box based adversarial method.
+"""

mindarmour/attacks/black/genetic_attack.py → mindarmour/adv_robustness/attacks/black/genetic_attack.py

@@ -17,13 +17,12 @@ Genetic-Attack.
 import numpy as np
 from scipy.special import softmax
 
-from mindarmour.attacks.attack import Attack
 from mindarmour.utils.logger import LogUtil
-from mindarmour.attacks.black.black_model import BlackModel
 from mindarmour.utils._check_param import check_numpy_param, check_model, \
     check_pair_numpy_param, check_param_type, check_value_positive, \
     check_int_positive, check_param_multi_types
-
+from ..attack import Attack
+from .black_model import BlackModel
 
 LOGGER = LogUtil.get_instance()
 TAG = 'GeneticAttack'

mindarmour/attacks/black/hop_skip_jump_attack.py → mindarmour/adv_robustness/attacks/black/hop_skip_jump_attack.py

@@ -16,12 +16,13 @@ Hop-skip-jump attack.
 """
 import numpy as np
 
-from mindarmour.attacks.attack import Attack
+
 from mindarmour.utils.logger import LogUtil
-from mindarmour.attacks.black.black_model import BlackModel
 from mindarmour.utils._check_param import check_pair_numpy_param, check_model, \
     check_numpy_param, check_int_positive, check_value_positive, \
     check_value_non_negative, check_param_type
+from ..attack import Attack
+from .black_model import BlackModel
 
 LOGGER = LogUtil.get_instance()
 TAG = 'HopSkipJumpAttack'

mindarmour/attacks/black/natural_evolutionary_strategy.py → mindarmour/adv_robustness/attacks/black/natural_evolutionary_strategy.py

@@ -18,12 +18,11 @@ import time
 import numpy as np
 from scipy.special import softmax
 
-from mindarmour.attacks.attack import Attack
 from mindarmour.utils.logger import LogUtil
-from mindarmour.attacks.black.black_model import BlackModel
 from mindarmour.utils._check_param import check_pair_numpy_param, check_model, \
     check_numpy_param, check_int_positive, check_value_positive, check_param_type
-
+from ..attack import Attack
+from .black_model import BlackModel
 
 LOGGER = LogUtil.get_instance()
 TAG = 'NES'

mindarmour/attacks/black/pointwise_attack.py → mindarmour/adv_robustness/attacks/black/pointwise_attack.py

@@ -16,13 +16,12 @@ Pointwise-Attack.
 """
 import numpy as np
 
-from mindarmour.attacks.attack import Attack
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.salt_and_pepper_attack import \
-    SaltAndPepperNoiseAttack
 from mindarmour.utils._check_param import check_model, check_pair_numpy_param, \
     check_int_positive, check_param_type
 from mindarmour.utils.logger import LogUtil
+from ..attack import Attack
+from .black_model import BlackModel
+from .salt_and_pepper_attack import SaltAndPepperNoiseAttack
 
 LOGGER = LogUtil.get_instance()
 TAG = 'PointWiseAttack'

mindarmour/attacks/black/pso_attack.py → mindarmour/adv_robustness/attacks/black/pso_attack.py

@@ -16,13 +16,12 @@ PSO-Attack.
 """
 import numpy as np
 
-from mindarmour.attacks.attack import Attack
 from mindarmour.utils.logger import LogUtil
-from mindarmour.attacks.black.black_model import BlackModel
 from mindarmour.utils._check_param import check_model, check_pair_numpy_param, \
     check_numpy_param, check_value_positive, check_int_positive, \
     check_param_type, check_equal_shape, check_param_multi_types
-
+from ..attack import Attack
+from .black_model import BlackModel
 
 LOGGER = LogUtil.get_instance()
 TAG = 'PSOAttack'

mindarmour/attacks/black/salt_and_pepper_attack.py → mindarmour/adv_robustness/attacks/black/salt_and_pepper_attack.py

@@ -18,12 +18,12 @@ import time
 
 import numpy as np
 
-from mindarmour.attacks.attack import Attack
-from mindarmour.attacks.black.black_model import BlackModel
 from mindarmour.utils._check_param import check_model, check_pair_numpy_param, \
     check_param_type, check_int_positive, check_param_multi_types
 from mindarmour.utils._check_param import normalize_value
 from mindarmour.utils.logger import LogUtil
+from ..attack import Attack
+from .black_model import BlackModel
 
 LOGGER = LogUtil.get_instance()
 TAG = 'SaltAndPepperNoise-Attack'

mindarmour/attacks/carlini_wagner.py → mindarmour/adv_robustness/attacks/carlini_wagner.py

@@ -19,13 +19,12 @@ import numpy as np
 from mindspore import Tensor
 from mindspore.nn import Cell
 
-from mindarmour.attacks.attack import Attack
 from mindarmour.utils.logger import LogUtil
 from mindarmour.utils._check_param import check_numpy_param, check_model, \
     check_pair_numpy_param, check_int_positive, check_param_type, \
     check_param_multi_types, check_value_positive, check_equal_shape
-from mindarmour.utils.util import GradWrap
-from mindarmour.utils.util import jacobian_matrix
+from mindarmour.utils.util import GradWrap, jacobian_matrix
+from .attack import Attack
 
 LOGGER = LogUtil.get_instance()
 TAG = 'CW'

mindarmour/attacks/deep_fool.py → mindarmour/adv_robustness/attacks/deep_fool.py

@@ -19,13 +19,12 @@ import numpy as np
 from mindspore import Tensor
 from mindspore.nn import Cell
 
-from mindarmour.attacks.attack import Attack
 from mindarmour.utils.logger import LogUtil
-from mindarmour.utils.util import GradWrap
-from mindarmour.utils.util import jacobian_matrix
+from mindarmour.utils.util import GradWrap, jacobian_matrix
 from mindarmour.utils._check_param import check_pair_numpy_param, check_model, \
     check_value_positive, check_int_positive, check_norm_level, \
     check_param_multi_types, check_param_type
+from .attack import Attack
 
 LOGGER = LogUtil.get_instance()
 TAG = 'DeepFool'

mindarmour/attacks/gradient_method.py → mindarmour/adv_robustness/attacks/gradient_method.py

@@ -19,16 +19,14 @@ from abc import abstractmethod
 import numpy as np
 
 from mindspore import Tensor
-from mindspore.nn import Cell
-from mindspore.nn import SoftmaxCrossEntropyWithLogits
+from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
 
-from mindarmour.attacks.attack import Attack
-from mindarmour.utils.util import WithLossCell
-from mindarmour.utils.util import GradWrapWithLoss
+from mindarmour.utils.util import WithLossCell, GradWrapWithLoss
 from mindarmour.utils.logger import LogUtil
 from mindarmour.utils._check_param import check_pair_numpy_param, check_model, \
     normalize_value, check_value_positive, check_param_multi_types, \
     check_norm_level, check_param_type
+from .attack import Attack
 
 LOGGER = LogUtil.get_instance()
 TAG = 'SingleGrad'

mindarmour/attacks/iterative_gradient_method.py → mindarmour/adv_robustness/attacks/iterative_gradient_method.py

@@ -17,18 +17,16 @@ from abc import abstractmethod
 import numpy as np
 from PIL import Image, ImageOps
 
-from mindspore.nn import SoftmaxCrossEntropyWithLogits
+from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
 from mindspore import Tensor
-from mindspore.nn import Cell
 
-from mindarmour.attacks.attack import Attack
-from mindarmour.attacks.gradient_method import FastGradientSignMethod
 from mindarmour.utils.logger import LogUtil
-from mindarmour.utils.util import WithLossCell
-from mindarmour.utils.util import GradWrapWithLoss
+from mindarmour.utils.util import WithLossCell, GradWrapWithLoss
 from mindarmour.utils._check_param import check_pair_numpy_param, \
     normalize_value, check_model, check_value_positive, check_int_positive, \
     check_param_type, check_norm_level, check_param_multi_types
+from .attack import Attack
+from .gradient_method import FastGradientSignMethod
 
 LOGGER = LogUtil.get_instance()
 TAG = 'IterGrad'

mindarmour/attacks/jsma.py → mindarmour/adv_robustness/attacks/jsma.py

@@ -19,14 +19,12 @@ import numpy as np
 from mindspore import Tensor
 from mindspore.nn import Cell
 
-from mindarmour.attacks.attack import Attack
-from mindarmour.utils.util import GradWrap
-from mindarmour.utils.util import jacobian_matrix
+from mindarmour.utils.util import GradWrap, jacobian_matrix
 from mindarmour.utils.logger import LogUtil
 from mindarmour.utils._check_param import check_pair_numpy_param, check_model, \
     check_param_type, check_int_positive, check_value_positive, \
     check_value_non_negative
-
+from .attack import Attack
 
 LOGGER = LogUtil.get_instance()
 TAG = 'JSMA'

mindarmour/attacks/lbfgs.py → mindarmour/adv_robustness/attacks/lbfgs.py

@@ -21,13 +21,12 @@ from mindspore import Tensor
 from mindspore.nn import Cell
 from mindspore.nn import SoftmaxCrossEntropyWithLogits
 
-from mindarmour.attacks.attack import Attack
 from mindarmour.utils.logger import LogUtil
-from mindarmour.utils.util import WithLossCell
-from mindarmour.utils.util import GradWrapWithLoss
+from mindarmour.utils.util import WithLossCell, GradWrapWithLoss
 from mindarmour.utils._check_param import check_pair_numpy_param, check_model, \
     check_int_positive, check_value_positive, check_param_type, \
     check_param_multi_types
+from .attack import Attack
 
 LOGGER = LogUtil.get_instance()
 TAG = 'LBFGS'

mindarmour/defenses/__init__.py → mindarmour/adv_robustness/defenses/__init__.py

+# Copyright 2020 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.
 """
 This module includes classical defense algorithms in defencing adversarial
 examples and enhancing model security and trustworthy.

mindarmour/defenses/adversarial_defense.py → mindarmour/adv_robustness/defenses/adversarial_defense.py

@@ -17,14 +17,13 @@ Adversarial Defense.
 import numpy as np
 
 from mindspore import Tensor
-from mindspore.nn import Cell
-from mindspore.nn.optim.momentum import Momentum
-from mindspore.nn import SoftmaxCrossEntropyWithLogits
+from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
 from mindspore.nn import WithLossCell, TrainOneStepCell
+from mindspore.nn.optim.momentum import Momentum
 
 from mindarmour.utils._check_param import check_pair_numpy_param, check_model, \
     check_param_in_range, check_param_type, check_param_multi_types
-from mindarmour.defenses.defense import Defense
+from .defense import Defense
 
 
 class AdversarialDefense(Defense):

mindarmour/defenses/natural_adversarial_defense.py → mindarmour/adv_robustness/defenses/natural_adversarial_defense.py

@@ -14,9 +14,8 @@
 """
 Natural Adversarial Defense.
 """
-from mindarmour.defenses.adversarial_defense import \
-    AdversarialDefenseWithAttacks
-from mindarmour.attacks.gradient_method import FastGradientSignMethod
+from ..attacks.gradient_method import FastGradientSignMethod
+from .adversarial_defense import AdversarialDefenseWithAttacks
 
 
 class NaturalAdversarialDefense(AdversarialDefenseWithAttacks):

mindarmour/defenses/projected_adversarial_defense.py → mindarmour/adv_robustness/defenses/projected_adversarial_defense.py

@@ -14,10 +14,8 @@
 """
 Projected Adversarial Defense.
 """
-from mindarmour.defenses.adversarial_defense import \
-    AdversarialDefenseWithAttacks
-from mindarmour.attacks.iterative_gradient_method import \
-    ProjectedGradientDescent
+from ..attacks.iterative_gradient_method import ProjectedGradientDescent
+from .adversarial_defense import AdversarialDefenseWithAttacks
 
 
 class ProjectedAdversarialDefense(AdversarialDefenseWithAttacks):

mindarmour/detectors/__init__.py → mindarmour/adv_robustness/detectors/__init__.py

+# Copyright 2020 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.
 """
 This module includes detector methods on distinguishing adversarial examples
 from benign examples.

mindarmour/adv_robustness/detectors/black/__init__.py

+# Copyright 2020 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.
+"""
+init black detector.
+"""

mindarmour/detectors/black/similarity_detector.py → mindarmour/adv_robustness/detectors/black/similarity_detector.py

@@ -20,11 +20,11 @@ import numpy as np
 from mindspore import Tensor
 from mindspore import Model
 
-from mindarmour.detectors.detector import Detector
 from mindarmour.utils.logger import LogUtil
 from mindarmour.utils._check_param import check_model, check_numpy_param, \
     check_int_positive, check_value_positive, check_param_type, \
     check_param_in_range
+from ..detector import Detector
 
 LOGGER = LogUtil.get_instance()
 TAG = 'SimilarityDetector'

mindarmour/detectors/ensemble_detector.py → mindarmour/adv_robustness/detectors/ensemble_detector.py

@@ -16,11 +16,10 @@ Ensemble Detector.
 """
 import numpy as np
 
-from mindarmour.detectors.detector import Detector
 from mindarmour.utils.logger import LogUtil
 from mindarmour.utils._check_param import check_numpy_param, \
     check_param_multi_types
-
+from .detector import Detector
 
 LOGGER = LogUtil.get_instance()
 TAG = 'EnsembleDetector'

mindarmour/detectors/mag_net.py → mindarmour/adv_robustness/detectors/mag_net.py

@@ -21,11 +21,11 @@ from scipy.special import softmax
 from mindspore import Tensor
 from mindspore import Model
 
-from mindarmour.detectors.detector import Detector
 from mindarmour.utils.logger import LogUtil
 from mindarmour.utils._check_param import check_numpy_param, check_model, \
     check_param_in_range, check_param_multi_types, check_int_positive, \
     check_value_positive
+from .detector import Detector
 
 LOGGER = LogUtil.get_instance()
 TAG = 'MagNet'

mindarmour/detectors/region_based_detector.py → mindarmour/adv_robustness/detectors/region_based_detector.py

@@ -21,12 +21,12 @@ import numpy as np
 from mindspore import Model
 from mindspore import Tensor
 
-from mindarmour.detectors.detector import Detector
 from mindarmour.utils.logger import LogUtil
 from mindarmour.utils._check_param import check_numpy_param, check_param_type, \
     check_pair_numpy_param, check_model, check_int_positive, \
     check_value_positive, check_value_non_negative, check_param_in_range, \
     check_equal_shape
+from .detector import Detector
 
 LOGGER = LogUtil.get_instance()
 TAG = 'RegionBasedDetector'

mindarmour/detectors/spatial_smoothing.py → mindarmour/adv_robustness/detectors/spatial_smoothing.py

@@ -20,11 +20,11 @@ from scipy import ndimage
 from mindspore import Model
 from mindspore import Tensor
 
-from mindarmour.detectors.detector import Detector
 from mindarmour.utils.logger import LogUtil
 from mindarmour.utils._check_param import check_model, check_numpy_param, \
     check_pair_numpy_param, check_int_positive, check_param_type, \
     check_param_in_range, check_equal_shape, check_value_positive
+from .detector import Detector
 
 LOGGER = LogUtil.get_instance()
 TAG = 'SpatialSmoothing'

mindarmour/evaluations/__init__.py → mindarmour/adv_robustness/evaluations/__init__.py

+# Copyright 2020 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.
 """
 This module includes various metrics to evaluate the result of attacks or
 defenses.

mindarmour/adv_robustness/evaluations/black/__init__.py

+# Copyright 2020 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.

mindarmour/fuzz_testing/__init__.py

+# Copyright 2020 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.
+"""
+This module provides a neuron coverage-gain based fuzz method to evaluate the
+robustness of given model.
+"""
+from .fuzzing import Fuzzer
+from .model_coverage_metrics import ModelCoverageMetrics
+
+__all__ = ['Fuzzer',
+           'ModelCoverageMetrics']

mindarmour/fuzzing/fuzzing.py → mindarmour/fuzz_testing/fuzzing.py

@@ -20,15 +20,15 @@ import numpy as np
 from mindspore import Model
 from mindspore import Tensor
 
-from mindarmour.fuzzing.model_coverage_metrics import ModelCoverageMetrics
 from mindarmour.utils._check_param import check_model, check_numpy_param, \
     check_param_multi_types, check_norm_level, check_param_in_range, \
     check_param_type, check_int_positive
-from mindarmour.fuzzing.image_transform import Contrast, Brightness, Blur, \
-    Noise, Translate, Scale, Shear, Rotate
-from mindarmour.attacks import FastGradientSignMethod, \
-    MomentumDiverseInputIterativeMethod, ProjectedGradientDescent
 from mindarmour.utils.logger import LogUtil
+from ..adv_robustness.attacks import FastGradientSignMethod, \
+    MomentumDiverseInputIterativeMethod, ProjectedGradientDescent
+from .image_transform import Contrast, Brightness, Blur, \
+    Noise, Translate, Scale, Shear, Rotate
+from .model_coverage_metrics import ModelCoverageMetrics
 
 LOGGER = LogUtil.get_instance()
 TAG = 'Fuzzer'
@@ -95,7 +95,7 @@ class Fuzzer:
         >>>                  {'method': 'FGSM', 'params': {'eps': 0.1, 'alpha': 0.1}}]
         >>> train_images = np.random.rand(32, 1, 32, 32).astype(np.float32)
         >>> model_fuzz_test = Fuzzer(model, train_images, 10, 1000)
-        >>> samples, labels, preds, strategies, report = model_fuzz_test.fuzzing(mutate_config, initial_seeds)
+        >>> samples, labels, preds, strategies, report = model_fuzz_test.fuzz_testing(mutate_config, initial_seeds)
     """
 
     def __init__(self, target_model, train_dataset, neuron_num, segmented_num=1000):
@@ -151,7 +151,7 @@ class Fuzzer:
                 'Rotate'. Thirdly, attack methods include: 'FGSM', 'PGD' and 'MDIIM'.
                 `mutate_config` must have method in the type of pixel value based
                 transform methods. The way of setting parameters for first and
-                second type methods can be seen in 'mindarmour/fuzzing/image_transform.py'.
+                second type methods can be seen in 'mindarmour/fuzz_testing/image_transform.py'.
                 For third type methods, you can refer to the corresponding class.
             initial_seeds (list[list]): Initial seeds used to generate mutated
                 samples. The format of initial seeds is [[image_data, label],
@@ -169,7 +169,7 @@ class Fuzzer:
                 Default: 20.
 
         Returns:
-            - list, mutated samples in fuzzing.
+            - list, mutated samples in fuzz_testing.
 
             - list, ground truth labels of mutated samples.
 
@@ -376,11 +376,11 @@ class Fuzzer:
     def _evaluate(self, fuzz_samples, gt_labels, fuzz_preds,
                   fuzz_strategies, metrics):
         """
-        Evaluate generated fuzzing samples in three dimention: accuracy,
+        Evaluate generated fuzz_testing samples in three dimention: accuracy,
         attack success rate and neural coverage.
 
         Args:
-            fuzz_samples (numpy.ndarray): Generated fuzzing samples according to seeds.
+            fuzz_samples (numpy.ndarray): Generated fuzz_testing samples according to seeds.
             gt_labels (numpy.ndarray): Ground Truth of seeds.
             fuzz_preds (numpy.ndarray): Predictions of generated fuzz samples.
             fuzz_strategies (numpy.ndarray): Mutate strategies of fuzz samples.

mindarmour/fuzzing/__init__.py

-"""
-This module includes various metrics to fuzzing the test of DNN.
-"""
-from .fuzzing import Fuzzer
-from .model_coverage_metrics import ModelCoverageMetrics
-
-__all__ = ['Fuzzer',
-           'ModelCoverageMetrics']

mindarmour/privacy/__init__.py

+# Copyright 2020 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.
+"""
+This module provides various methods to protect the privacy of given model, and
+also provides some methods to evaluate the risk of privacy leakage of given
+model.
+"""

mindarmour/diff_privacy/__init__.py → mindarmour/privacy/diff_privacy/__init__.py

+# Copyright 2020 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.
 """
-This module provide Differential Privacy feature to protect user privacy.
+This module provides Differential Privacy feature to protect user privacy.
 """
 from .mechanisms.mechanisms import NoiseGaussianRandom
 from .mechanisms.mechanisms import NoiseAdaGaussianRandom
@@ -11,7 +24,6 @@ from .monitor.monitor import RDPMonitor
 from .monitor.monitor import ZCDPMonitor
 from .optimizer.optimizer import DPOptimizerClassFactory
 from .train.model import DPModel
-from .evaluation.membership_inference import MembershipInference
 
 __all__ = ['NoiseGaussianRandom',
            'NoiseAdaGaussianRandom',
@@ -22,5 +34,4 @@ __all__ = ['NoiseGaussianRandom',
            'RDPMonitor',
            'ZCDPMonitor',
            'DPOptimizerClassFactory',
-           'DPModel',
-           'MembershipInference']
+           'DPModel']

mindarmour/privacy/diff_privacy/mechanisms/__init__.py

+# Copyright 2020 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.
+"""
+init mechanisms.
+"""

mindarmour/privacy/diff_privacy/monitor/__init__.py

+# Copyright 2020 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.
+"""
+init monitor.
+"""

mindarmour/privacy/diff_privacy/optimizer/__init__.py

+# Copyright 2020 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.
+"""
+init optimizer.
+"""

mindarmour/diff_privacy/optimizer/optimizer.py → mindarmour/privacy/diff_privacy/optimizer/optimizer.py

@@ -22,9 +22,9 @@ from mindspore.ops import functional as F
 from mindspore.common import dtype as mstype
 
 from mindarmour.utils.logger import LogUtil
-from mindarmour.diff_privacy import NoiseMechanismsFactory
-from mindarmour.diff_privacy.mechanisms.mechanisms import _MechanismsParamsUpdater
 from mindarmour.utils._check_param import check_int_positive
+from ..mechanisms.mechanisms import NoiseMechanismsFactory
+from ..mechanisms.mechanisms import _MechanismsParamsUpdater
 
 LOGGER = LogUtil.get_instance()
 TAG = 'DP optimizer'

mindarmour/privacy/diff_privacy/train/__init__.py

+# Copyright 2020 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.
+"""
+init train.
+"""

mindarmour/diff_privacy/train/model.py → mindarmour/privacy/diff_privacy/train/model.py

@@ -48,10 +48,9 @@ from mindspore.nn import Cell
 from mindspore import ParameterTuple
 
 from mindarmour.utils.logger import LogUtil
-from mindarmour.diff_privacy.mechanisms.mechanisms import \
-    _MechanismsParamsUpdater
 from mindarmour.utils._check_param import check_value_positive, check_param_type
 from mindarmour.utils._check_param import check_int_positive
+from ..mechanisms.mechanisms import _MechanismsParamsUpdater
 
 LOGGER = LogUtil.get_instance()
 TAG = 'DP model'

mindarmour/privacy/evaluation/__init__.py

+# Copyright 2020 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.
+"""
+This module provides some methods to evaluate the risk of privacy leakage of
+given model.
+"""
+
+from .membership_inference import MembershipInference
+
+__all__ = ['MembershipInference']

mindarmour/utils/__init__.py

 """
-Util methods of MindArmour."""
+Util methods of MindArmour.
+"""
 from .logger import LogUtil
 from .util import GradWrap
 from .util import GradWrapWithLoss

mindarmour/utils/_check_param.py

@@ -14,7 +14,7 @@
 """ check parameters for MindArmour. """
 import numpy as np
 
-from mindarmour.utils.logger import LogUtil
+from .logger import LogUtil
 
 LOGGER = LogUtil.get_instance()
 TAG = 'check parameters'

mindarmour/utils/util.py

@@ -17,7 +17,7 @@ from mindspore import Tensor
 from mindspore.nn import Cell
 from mindspore.ops.composite import GradOperation
 
-from mindarmour.utils.logger import LogUtil
+from .logger import LogUtil
 
 LOGGER = LogUtil.get_instance()
 TAG = 'util'

tests/st/resnet50/test_cifar10_attack_fgsm.py

@@ -29,7 +29,7 @@ from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 
-from mindarmour.attacks.gradient_method import FastGradientSignMethod
+from mindarmour.adv_robustness.attacks import FastGradientSignMethod
 
 from resnet_cifar10 import resnet50_cifar10
 

tests/ut/python/attacks/black/test_genetic_attack.py

@@ -21,8 +21,8 @@ from mindspore import Tensor
 from mindspore import context
 from mindspore.nn import Cell
 
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.genetic_attack import GeneticAttack
+from mindarmour import BlackModel
+from mindarmour.adv_robustness.attacks import GeneticAttack
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 

tests/ut/python/attacks/black/test_hsja.py

@@ -20,8 +20,8 @@ from mindspore import Tensor
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.hop_skip_jump_attack import HopSkipJumpAttack
+from mindarmour import BlackModel
+from mindarmour.adv_robustness.attacks import HopSkipJumpAttack
 from mindarmour.utils.logger import LogUtil
 
 sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),

tests/ut/python/attacks/black/test_nes.py

@@ -20,8 +20,8 @@ from mindspore import Tensor
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.natural_evolutionary_strategy import NES
+from mindarmour import BlackModel
+from mindarmour.adv_robustness.attacks import NES
 from mindarmour.utils.logger import LogUtil
 
 sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),

tests/ut/python/attacks/black/test_pointwise_attack.py

@@ -23,8 +23,8 @@ from mindspore import Tensor
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
-from mindarmour.attacks.black.black_model import BlackModel
-from mindarmour.attacks.black.pointwise_attack import PointWiseAttack
+from mindarmour import BlackModel
+from mindarmour.adv_robustness.attacks import PointWiseAttack
 from mindarmour.utils.logger import LogUtil
 
 sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),

tests/ut/python/attacks/black/test_pso_attack.py

@@ -22,8 +22,8 @@ import mindspore.nn as nn
 from mindspore.nn import Cell
 from mindspore import context
 
-from mindarmour.attacks.black.pso_attack import PSOAttack
-from mindarmour.attacks.black.black_model import BlackModel
+from mindarmour.adv_robustness.attacks import PSOAttack
+from mindarmour import BlackModel
 
 
 # for user

tests/ut/python/attacks/black/test_salt_and_pepper_attack.py

@@ -22,9 +22,8 @@ from mindspore import Tensor
 from mindspore.nn import Cell
 from mindspore import context
 
-from mindarmour.attacks.black.salt_and_pepper_attack import \
-    SaltAndPepperNoiseAttack
-from mindarmour.attacks.black.black_model import BlackModel
+from mindarmour import BlackModel
+from mindarmour.adv_robustness.attacks import SaltAndPepperNoiseAttack
 
 context.set_context(mode=context.GRAPH_MODE)
 context.set_context(device_target="Ascend")

tests/ut/python/attacks/test_batch_generate_attack.py

@@ -21,7 +21,7 @@ import mindspore.ops.operations as P
 from mindspore.nn import Cell
 import mindspore.context as context
 
-from mindarmour.attacks.gradient_method import FastGradientMethod
+from mindarmour.adv_robustness.attacks import FastGradientMethod
 
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")

tests/ut/python/attacks/test_cw.py

@@ -21,7 +21,7 @@ import mindspore.ops.operations as M
 from mindspore.nn import Cell
 from mindspore import context
 
-from mindarmour.attacks.carlini_wagner import CarliniWagnerL2Attack
+from mindarmour.adv_robustness.attacks import CarliniWagnerL2Attack
 
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")

tests/ut/python/attacks/test_deep_fool.py

@@ -17,12 +17,12 @@ DeepFool-Attack test.
 import numpy as np
 import pytest
 
-import mindspore.ops.operations as M
+import mindspore.ops.operations as P
 from mindspore.nn import Cell
 from mindspore import context
 from mindspore import Tensor
 
-from mindarmour.attacks.deep_fool import DeepFool
+from mindarmour.adv_robustness.attacks import DeepFool
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
@@ -41,7 +41,7 @@ class Net(Cell):
         Introduce the layers used for network construction.
         """
         super(Net, self).__init__()
-        self._softmax = M.Softmax()
+        self._softmax = P.Softmax()
 
     def construct(self, inputs):
         """