Merge remote-tracking branch 'origin' into dev_1.20.0

Author: beat-buesser

art/evaluations/security_curve/security_curve.py

@@ -165,7 +165,12 @@ def plot(self) -> None:  # pragma: no cover
         from matplotlib import pyplot as plt
 
         plt.plot(self.eps_list, self.accuracy_adv_list, label="adversarial", marker="o")
-        plt.plot([self.eps_list[0], self.eps_list[-1]], [self.accuracy, self.accuracy], linestyle="--", label="benign")
+        plt.plot(
+            [self.eps_list[0], self.eps_list[-1]],  # type: ignore
+            [self.accuracy, self.accuracy],  # type: ignore
+            linestyle="--",
+            label="benign",
+        )
         plt.legend()
         plt.xlabel("Attack budget eps")
         plt.ylabel("Accuracy")

art/visualization.py

@@ -150,7 +150,7 @@ def plot_3d(
     for i, coord in enumerate(points):
         try:
             color_point = labels[i]
-            axis.scatter3D(coord[0], coord[1], coord[2], color=colors[color_point])
+            axis.scatter3D(coord[0], coord[1], coord[2], color=colors[color_point])  # type: ignore
         except IndexError:
             raise ValueError(
                 "Labels outside the range. Should start from zero and be sequential there after"

requirements_test.txt

@@ -4,7 +4,7 @@
 
 numpy>=1.18.5,<1.27
 scipy==1.10.1
-matplotlib==3.7.1
+matplotlib==3.9.4
 scikit-learn==1.6.1
 six==1.17.0
 Pillow==11.1.0
@@ -44,19 +44,19 @@ xgboost==2.1.1
 kornia~=0.8.0
 tensorboardX==2.6.2.2
 lief==0.15.1
-jax[cpu]==0.4.26
+jax[cpu]==0.4.30
 
 # tests and style checking
 pytest~=8.3.2
 pytest-mock~=3.14.0
-pytest-cov~=6.0.0
+pytest-cov~=6.1.1
 pylint==3.2.6
 mypy==1.11.1
 pycodestyle==2.12.1
 black==25.1.0
 ruff==0.9.3
-types-six==1.16.21.9
-types-PyYAML==6.0.12.20240917
+types-six==1.17.0.20250515
+types-PyYAML==6.0.12.20250516
 types-setuptools==80.9.0.20250529
 
 # other

tests/attacks/test_steal_now_attack_later.py

@@ -66,6 +66,8 @@ def collect_patches_from_images(detector, imgs):
                 pos_matrix[:, 2] = torch.clamp_max(pos_matrix[:, 2], imgs.shape[3])
                 pos_matrix[:, 3] = torch.clamp_max(pos_matrix[:, 3], imgs.shape[2])
                 for pos_m in pos_matrix:
+                    if pos_m[3] - pos_m[1] == 0 or pos_m[2] - pos_m[0] == 0:
+                        continue
                     p = imgs[i, :, pos_m[1] : pos_m[3], pos_m[0] : pos_m[2]]
                     patch.append(p.to(detector.device))
 
@@ -165,7 +167,7 @@ def _loader(path):
         attack = SNAL(
             object_detector,
             eps=16.0 / 255.0,
-            max_iter=10,
+            max_iter=5,
             num_grid=10,
             candidates=candidates_list,
             collector=collect_patches_from_images,