Fix an issue.

examples/model_security/model_attacks/black_box/mnist_attack_pointwise.py

@@ -110,7 +110,7 @@ def test_pointwise_attack_on_mnist():
     test_labels_onehot = np.eye(10)[true_labels]
     attack_evaluate = AttackEvaluate(np.concatenate(test_images),
                                      test_labels_onehot, adv_data,
-                                     adv_preds, targeted=is_target,
+                                     np.array(adv_preds), targeted=is_target,
                                      target_label=targeted_labels)
     LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
                 attack_evaluate.mis_classification_rate())

examples/model_security/model_defenses/mnist_evaluation.py

@@ -39,6 +39,7 @@ from examples.common.dataset.data_processing import generate_mnist_dataset
 from examples.common.networks.lenet5.lenet5_net import LeNet5
 
 LOGGER = LogUtil.get_instance()
+LOGGER.set_level('INFO')
 TAG = 'Defense_Evaluate_Example'
 
 

examples/model_security/model_defenses/mnist_similarity_detector.py

@@ -30,6 +30,7 @@ from examples.common.dataset.data_processing import generate_mnist_dataset
 from examples.common.networks.lenet5.lenet5_net import LeNet5
 
 LOGGER = LogUtil.get_instance()
+LOGGER.set_level('INFO')
 TAG = 'Similarity Detector test'
 
 

mindarmour/adv_robustness/attacks/deep_fool.py

@@ -152,10 +152,12 @@ class DeepFool(Attack):
         Generate adversarial examples based on input samples and original labels.
 
         Args:
-            inputs (Union[numpy.ndarray, tuple]): Input samples. The format of inputs can be (inputs1, input2, ...) \
-                or only one array if model_type='detection'
-            labels (Union[numpy.ndarray, tuple]): Original labels. The format of labels should be \
-                (gt_boxes, gt_labels) if model_type='detection'.
+            inputs (Union[numpy.ndarray, tuple]): Input samples. The format of inputs should be numpy.ndarray if
+                model_type='classification'. The format of inputs can be (input1, input2, ...) or only one array if
+                model_type='detection'.
+            labels (Union[numpy.ndarray, tuple]): Targeted labels or ground-truth labels. The format of labels should
+                be numpy.ndarray if model_type='classification'. The format of labels should be (gt_boxes, gt_labels)
+                if model_type='detection'.
 
         Returns:
             numpy.ndarray, adversarial examples.