Merge remote-tracking branch 'origin/main' into dev_1.15.0

.github/workflows/dockerhub.yml

@@ -24,22 +24,22 @@ jobs:
         uses: actions/checkout@v3
 
       - name: Log in to Docker Hub
-        uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a
+        uses: docker/login-action@465a07811f14bebb1938fbed4728c6a1ff8901fc
         with:
           username: ${{ secrets.DOCKER_HUB_USERNAME }}
           password: ${{ secrets.DOCKER_HUB_ACCESS_TOKEN }}
 
       - name: Extract metadata (tags, labels) for Docker
         id: meta
-        uses: docker/metadata-action@c4ee3adeed93b1fa6a762f209fb01608c1a22f1e
+        uses: docker/metadata-action@818d4b7b91585d195f67373fd9cb0332e31a7175
         with:
           images: adversarialrobustnesstoolbox/releases
           tags: |
             type=raw,value={{branch}}-1.14.1-{{sha}}
             type=semver,pattern={{version}}
 
       - name: Build and push Docker image
-        uses: docker/build-push-action@3b5e8027fcad23fda98b2e3ac259d8d67585f671
+        uses: docker/build-push-action@2eb1c1961a95fc15694676618e422e8ba1d63825
         with:
           context: .
           push: true

art/attacks/evasion/adversarial_patch/adversarial_patch_pytorch.py

@@ -123,7 +123,9 @@ def __init__(
 
         torch_version = list(map(int, torch.__version__.lower().split("+", maxsplit=1)[0].split(".")))
         torchvision_version = list(map(int, torchvision.__version__.lower().split("+", maxsplit=1)[0].split(".")))
-        assert torch_version[0] >= 1 and torch_version[1] >= 7, "AdversarialPatchPyTorch requires torch>=1.7.0"
+        assert (
+            torch_version[0] >= 1 and torch_version[1] >= 7 or (torch_version[0] >= 2)
+        ), "AdversarialPatchPyTorch requires torch>=1.7.0"
         assert (
             torchvision_version[0] >= 0 and torchvision_version[1] >= 8
         ), "AdversarialPatchPyTorch requires torchvision>=0.8.0"

examples/adversarial_training_cifar10.py

@@ -7,6 +7,10 @@
 
 import logging
 
+import tensorflow as tf
+
+tf.compat.v1.disable_eager_execution()
+
 from keras.models import Sequential
 from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation, Dropout
 import numpy as np

examples/application_object_detection.py

@@ -129,11 +129,24 @@ def plot_image_with_boxes(img, boxes, pred_cls):
 
     for i in range(len(boxes)):
         # Draw Rectangle with the coordinates
-        cv2.rectangle(img, boxes[i][0], boxes[i][1], color=(0, 255, 0), thickness=rect_th)
 
+        cv2.rectangle(
+            img,
+            (int(boxes[i][0][0]), int(boxes[i][0][1])),
+            (int(boxes[i][1][0]), int(boxes[i][1][1])),
+            color=(0, 255, 0),
+            thickness=rect_th,
+        )
         # Write the prediction class
-        cv2.putText(img, pred_cls[i], boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0, 255, 0), thickness=text_th)
-
+        cv2.putText(
+            img,
+            pred_cls[i],
+            (int(boxes[i][0][0]), int(boxes[i][0][1])),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            text_size,
+            (0, 255, 0),
+            thickness=text_th,
+        )
     plt.axis("off")
     plt.imshow(img.astype(np.uint8), interpolation="nearest")
     plt.show()

examples/get_started_fasterrcnn.py

@@ -158,11 +158,24 @@ def plot_image_with_boxes(img, boxes, pred_cls):
 
     for i in range(len(boxes)):
         # Draw Rectangle with the coordinates
-        cv2.rectangle(img, boxes[i][0], boxes[i][1], color=(0, 255, 0), thickness=rect_th)
 
+        cv2.rectangle(
+            img,
+            (int(boxes[i][0][0]), int(boxes[i][0][1])),
+            (int(boxes[i][1][0]), int(boxes[i][1][1])),
+            color=(0, 255, 0),
+            thickness=rect_th,
+        )
         # Write the prediction class
-        cv2.putText(img, pred_cls[i], boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0, 255, 0), thickness=text_th)
-
+        cv2.putText(
+            img,
+            pred_cls[i],
+            (int(boxes[i][0][0]), int(boxes[i][0][1])),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            text_size,
+            (0, 255, 0),
+            thickness=text_th,
+        )
     plt.axis("off")
     plt.imshow(img.astype(np.uint8), interpolation="nearest")
     plt.show()
@@ -215,7 +228,7 @@ def append_loss_history(loss_history, output):
             "batch_size": 1,
             "image_file": "banner-diverse-group-of-people-2.jpg",
             "resume": False,
-            "path": "xp/",
+            "path": "",
         }
 
     pp = pprint.PrettyPrinter(indent=4)
@@ -280,3 +293,14 @@ def append_loss_history(loss_history, output):
             file.write(json.dumps(loss_history))
 
         np.save(os.path.join(config["path"], "patch"), attack._patch)
+
+    predictions_adv = frcnn.predict(x=x_patch)
+
+    for i in range(image.shape[0]):
+        print("\nPredictions adversarial image {}:".format(i))
+
+        # Process predictions
+        predictions_adv_class, predictions_adv_boxes, predictions_adv_class = extract_predictions(predictions_adv[i])
+
+        # Plot predictions
+        plot_image_with_boxes(img=x_patch[i].copy(), boxes=predictions_adv_boxes, pred_cls=predictions_adv_class)

examples/get_started_keras.py

@@ -10,7 +10,7 @@
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
 from tensorflow.keras.losses import categorical_crossentropy
-from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.optimizers.legacy import Adam
 import numpy as np
 
 from art.attacks.evasion import FastGradientMethod

notebooks/poisoning_defense_neural_cleanse.ipynb

@@ -55,7 +55,11 @@
     "from art.utils import load_mnist, preprocess\n",
     "from art.defences.detector.poison import ActivationDefence\n",
     "from art.defences.transformer.poisoning import NeuralCleanse\n",
-    "from art.estimators.certification.neural_cleanse import KerasNeuralCleanse\n"
+    "from art.estimators.poison_mitigation import KerasNeuralCleanse\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "if tf.executing_eagerly():\n",
+    "    tf.compat.v1.disable_eager_execution()"
    ]
   },
   {

requirements_test.txt

@@ -39,7 +39,7 @@ GPy==1.10.0
 lightgbm==3.3.5
 xgboost==1.7.5
 
-kornia~=0.6.10
+kornia~=0.6.12
 tensorboardX==2.6
 lief==0.12.3
 jax[cpu]==0.4.8