add an example of fuzz testing and model enhense

examples/ai_fuzzer/fuzz_testing_and_model_enhense.py

@@ -0,0 +1,169 @@
+# 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.
+"""
+An example of fuzz testing and then enhance non-robustness model.
+"""
+import random
+import numpy as np
+
+import mindspore
+from mindspore import Model
+from mindspore import context
+from mindspore import Tensor
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.nn import SoftmaxCrossEntropyWithLogits
+from mindspore.nn.optim.momentum import Momentum
+
+from mindarmour.adv_robustness.defenses import AdversarialDefense
+from mindarmour.fuzz_testing import Fuzzer
+from mindarmour.fuzz_testing import ModelCoverageMetrics
+from mindarmour.utils.logger import LogUtil
+
+from examples.common.dataset.data_processing import generate_mnist_dataset
+from examples.common.networks.lenet5.lenet5_net import LeNet5
+
+LOGGER = LogUtil.get_instance()
+TAG = 'Fuzz_testing and enhance model'
+LOGGER.set_level('INFO')
+
+
+def example_lenet_mnist_fuzzing():
+    """
+    An example of fuzz testing and then enhance the non-robustness model.
+    """
+    # upload trained network
+    ckpt_path = '../common/networks/lenet5/trained_ckpt_file/lenet_m1-10_1250.ckpt'
+    net = LeNet5()
+    load_dict = load_checkpoint(ckpt_path)
+    load_param_into_net(net, load_dict)
+    model = Model(net)
+    mutate_config = [{'method': 'Blur',
+                      'params': {'auto_param': [True]}},
+                     {'method': 'Contrast',
+                      'params': {'auto_param': [True]}},
+                     {'method': 'Translate',
+                      'params': {'auto_param': [True]}},
+                     {'method': 'Brightness',
+                      'params': {'auto_param': [True]}},
+                     {'method': 'Noise',
+                      'params': {'auto_param': [True]}},
+                     {'method': 'Scale',
+                      'params': {'auto_param': [True]}},
+                     {'method': 'Shear',
+                      'params': {'auto_param': [True]}},
+                     {'method': 'FGSM',
+                      'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1]}}
+                     ]
+
+    # get training data
+    data_list = "../common/dataset/MNIST/train"
+    batch_size = 32
+    ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
+    train_images = []
+    for data in ds.create_tuple_iterator(output_numpy=True):
+        images = data[0].astype(np.float32)
+        train_images.append(images)
+    train_images = np.concatenate(train_images, axis=0)
+
+    # initialize fuzz test with training dataset
+    model_coverage_test = ModelCoverageMetrics(model, 10, 1000, train_images)
+
+    # fuzz test with original test data
+    # get test data
+    data_list = "../common/dataset/MNIST/test"
+    batch_size = 32
+    init_samples = 5000
+    max_iters = 50000
+    mutate_num_per_seed = 10
+    ds = generate_mnist_dataset(data_list, batch_size, num_samples=init_samples,
+                                sparse=False)
+    test_images = []
+    test_labels = []
+    for data in ds.create_tuple_iterator(output_numpy=True):
+        images = data[0].astype(np.float32)
+        labels = data[1]
+        test_images.append(images)
+        test_labels.append(labels)
+    test_images = np.concatenate(test_images, axis=0)
+    test_labels = np.concatenate(test_labels, axis=0)
+    initial_seeds = []
+
+    # make initial seeds
+    for img, label in zip(test_images, test_labels):
+        initial_seeds.append([img, label])
+
+    model_coverage_test.calculate_coverage(
+        np.array(test_images[:100]).astype(np.float32))
+    LOGGER.info(TAG, 'KMNC of test dataset before fuzzing is : %s',
+                model_coverage_test.get_kmnc())
+    LOGGER.info(TAG, 'NBC of test dataset before fuzzing is : %s',
+                model_coverage_test.get_nbc())
+    LOGGER.info(TAG, 'SNAC of test dataset before fuzzing is : %s',
+                model_coverage_test.get_snac())
+
+    model_fuzz_test = Fuzzer(model, train_images, 10, 1000)
+    gen_samples, gt, _, _, metrics = model_fuzz_test.fuzzing(mutate_config,
+                                                             initial_seeds,
+                                                             eval_metrics='auto',
+                                                             max_iters=max_iters,
+                                                             mutate_num_per_seed=mutate_num_per_seed)
+
+    if metrics:
+        for key in metrics:
+            LOGGER.info(TAG, key + ': %s', metrics[key])
+
+    def split_dataset(image, label, proportion):
+        """
+        Split the generated fuzz data into train and test set.
+        """
+        indices = np.arange(len(image))
+        random.shuffle(indices)
+        train_length = int(len(image) * proportion)
+        train_image = [image[i] for i in indices[:train_length]]
+        train_label = [label[i] for i in indices[:train_length]]
+        test_image = [image[i] for i in indices[:train_length]]
+        test_label = [label[i] for i in indices[:train_length]]
+        return train_image, train_label, test_image, test_label
+
+    train_image, train_label, test_image, test_label = split_dataset(
+        gen_samples, gt, 0.7)
+
+    # load model B and test it on the test set
+    ckpt_path = '../common/networks/lenet5/trained_ckpt_file/lenet_m2-10_1250.ckpt'
+    net = LeNet5()
+    load_dict = load_checkpoint(ckpt_path)
+    load_param_into_net(net, load_dict)
+    model_b = Model(net)
+    pred_b = model_b.predict(Tensor(test_image, dtype=mindspore.float32)).asnumpy()
+    acc_b = np.sum(np.argmax(pred_b, axis=1) == np.argmax(test_label, axis=1)) / len(test_label)
+    print('Accuracy of model B on test set is ', acc_b)
+
+    # enhense model robustness
+    lr = 0.001
+    momentum = 0.9
+    loss_fn = SoftmaxCrossEntropyWithLogits(Sparse=True)
+    optimizer = Momentum(net.trainable_params(), lr, momentum)
+
+    adv_defense = AdversarialDefense(net, loss_fn, optimizer)
+    adv_defense.batch_defense(np.array(train_image).astype(np.float32),
+                              np.argmax(train_label, axis=1).astype(np.int32))
+    preds_en = net(Tensor(test_image, dtype=mindspore.float32)).asnumpy()
+    acc_en = np.sum(np.argmax(preds_en, axis=1) == np.argmax(test_label, axis=1)) / len(test_label)
+    print('Accuracy of enhensed model on test set is ', acc_en)
+
+
+if __name__ == '__main__':
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    example_lenet_mnist_fuzzing()

examples/common/dataset/data_processing.py

@@ -21,12 +21,12 @@ import mindspore.common.dtype as mstype
 
 
 def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1,
-                           num_parallel_workers=1, sparse=True):
+                           num_samples=None, num_parallel_workers=1, sparse=True):
     """
     create dataset for training or testing
     """
     # define dataset
-    ds1 = ds.MnistDataset(data_path)
+    ds1 = ds.MnistDataset(data_path, num_samples=num_samples)
 
     # define operation parameters
     resize_height, resize_width = 32, 32

mindarmour/fuzz_testing/fuzzing.py

@@ -204,7 +204,7 @@ class Fuzzer:
                 `self._attack_param_checklists`.
             initial_seeds (list[list]): Initial seeds used to generate mutated
                 samples. The format of initial seeds is [[image_data, label],
-                [...], ...].
+                [...], ...] and the label must be one-hot.
             coverage_metric (str): Model coverage metric of neural networks. All
                 supported metrics are: 'KMNC', 'NBC', 'SNAC'. Default: 'KMNC'.
             eval_metrics (Union[list, tuple, str]): Evaluation metrics. If the