Fix dependencies

Signed-off-by: Beat Buesser <[email protected]>

Author: beat-buesser

.github/actions/yolo/run.sh

@@ -8,4 +8,7 @@ if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed estimators/object_detection/te
 pytest --cov-report=xml --cov=art --cov-append -q -vv tests/estimators/object_detection/test_object_seeker_yolo.py --framework=pytorch --durations=0
 if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed estimators/object_detection/test_object_seeker_yolo tests"; fi
 
+pytest --cov-report=xml --cov=art --cov-append -q -vv tests/attacks/test_overload_attack.py --framework=pytorch --durations=0
+if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed attacks/test_overload_attack tests"; fi
+
 exit ${exit_code}

examples/get_started_yolo.py

@@ -147,6 +147,8 @@ def plot_image_with_boxes(img, boxes, pred_cls, title):
     text_th = 3
     rect_th = 1
 
+    img = img.copy()
+
     for i in range(len(boxes)):
         cv2.rectangle(
             img,
@@ -206,8 +208,10 @@ def forward(self, x, targets=None):
             else:
                 return self.model(x)
 
-    model_path = "./yolov3.cfg"
-    weights_path = "./yolov3.weights"
+    # model_path = "./yolov3.cfg"
+    # weights_path = "./yolov3.weights"
+    model_path = "/tmp/PyTorch-YOLOv3/config/yolov3.cfg"
+    weights_path = "/tmp/PyTorch-YOLOv3/weights/yolov3.weights"
     model = load_model(model_path=model_path, weights_path=weights_path)
 
     model = Yolo(model)

tests/attacks/evasion/test_overload_attack.py

@@ -0,0 +1,122 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2024
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+# import logging
+#
+# import pytest
+
+from art.attacks.evasion.overload.overload import OverloadPyTorch
+
+from tests.utils import ARTTestException
+from tests.estimators.object_detection.conftest import *
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_generate(art_warning, get_pytorch_yolo):
+    try:
+        from io import BytesIO
+        from PIL import Image
+        import requests
+        import torch
+        from ultralytics import YOLO
+
+        threshold = 0.85
+
+        object_detector, _, _ = get_pytorch_yolo
+        object_detector.set_params(input_shape=(3, 640, 640))
+
+        # Download a sample image
+        target = "https://ultralytics.com/images/zidane.jpg"
+        response = requests.get(target)
+        org_img = np.asarray(Image.open(BytesIO(response.content)).resize((640, 640)))
+        x_255 = np.stack([org_img.transpose((2, 0, 1))], axis=0).astype(np.uint8)
+        x = x_255.astype(np.float32) / 255.0
+
+        y_pred = object_detector.predict(x=x)
+
+        attack = OverloadPyTorch(
+            object_detector, eps=16.0 / 255.0, max_iter=10, num_grid=10, batch_size=1, threshold=threshold
+        )
+
+        x_adv = attack.generate(x=x, y=y_pred)
+
+        assert x.shape == x_adv.shape
+        assert np.min(x_adv) >= 0.0
+        assert np.max(x_adv) <= 1.0
+
+        y_pred_adv = object_detector.predict(x=x_adv)
+
+        scores_list = list(y_pred[0]["scores"])
+        scores_list_adv = list(y_pred_adv[0]["scores"])
+
+        scores_list_filtered = [scores_list.index(x) for x in scores_list if x > threshold]
+        scores_list_adv_filtered = [scores_list_adv.index(x) for x in scores_list_adv if x > threshold]
+
+        assert len(scores_list_filtered) == 11
+        assert len(scores_list_adv_filtered) == 178
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_check_params(art_warning, get_pytorch_yolo):
+    try:
+        object_detector, _, _ = get_pytorch_yolo
+        object_detector.set_params(input_shape=(3, 640, 640))
+
+        with pytest.raises(ValueError):
+            _ = OverloadPyTorch(
+                estimator=object_detector, eps=-1.0, max_iter=5, num_grid=10, batch_size=1, threshold=0.5
+            )
+        with pytest.raises(ValueError):
+            _ = OverloadPyTorch(
+                estimator=object_detector, eps=2.0, max_iter=5, num_grid=10, batch_size=1, threshold=0.5
+            )
+        with pytest.raises(TypeError):
+            _ = OverloadPyTorch(
+                estimator=object_detector, eps=8 / 255.0, max_iter=1.0, num_grid=10, batch_size=1, threshold=0.5
+            )
+        with pytest.raises(ValueError):
+            _ = OverloadPyTorch(
+                estimator=object_detector, eps=8 / 255.0, max_iter=0, num_grid=10, batch_size=1, threshold=0.5
+            )
+        with pytest.raises(TypeError):
+            _ = OverloadPyTorch(
+                estimator=object_detector, eps=8 / 255.0, max_iter=5, num_grid=1.0, batch_size=1, threshold=0.5
+            )
+        with pytest.raises(ValueError):
+            _ = OverloadPyTorch(
+                estimator=object_detector, eps=8 / 255.0, max_iter=5, num_grid=0, batch_size=1, threshold=0.5
+            )
+        with pytest.raises(TypeError):
+            _ = OverloadPyTorch(
+                estimator=object_detector, eps=8 / 255.0, max_iter=5, num_grid=10, batch_size=1.0, threshold=0.5
+            )
+        with pytest.raises(ValueError):
+            _ = OverloadPyTorch(
+                estimator=object_detector, eps=8 / 255.0, max_iter=5, num_grid=0, batch_size=0, threshold=0.5
+            )
+        with pytest.raises(ValueError):
+            _ = OverloadPyTorch(
+                estimator=object_detector, eps=8 / 255.0, max_iter=5, num_grid=0, batch_size=1, threshold=1.5
+            )
+
+    except ARTTestException as e:
+        art_warning(e)