Merge remote-tracking branch 'origin/main' into dev_1.19.2

Author: beat-buesser

.github/actions/deepspeech-v3/Dockerfile

@@ -1,5 +1,5 @@
 # Get base image
-FROM python:3.10.13-slim-bullseye
+FROM python:3.10.13-slim-bookworm
 
 # Set to install things in non-interactive mode
 ENV DEBIAN_FRONTEND noninteractive

.github/actions/espresso/Dockerfile

@@ -1,5 +1,5 @@
 # Get base image
-FROM python:3.7.17-slim-bullseye
+FROM python:3.7.17-slim-bookworm
 
 # Set to install things in non-interactive mode
 ENV DEBIAN_FRONTEND noninteractive

.github/actions/goturn/Dockerfile

@@ -1,5 +1,7 @@
 # Get base image
-FROM python:3.8.19-slim-bullseye
+FROM python:3.8.19-slim-bookworm
+
+ENV MONO_TLS_PROVIDER=legacy
 
 # Install system wide software
 RUN apt-get update \

.github/actions/tfv2-faster-rcnn/Dockerfile

@@ -1,5 +1,5 @@
 # Get base from a tensorflow image
-FROM tensorflow/tensorflow:2.10.1
+FROM tensorflow/tensorflow:2.13.0
 
 # Set to install things in non-interactive mode
 ENV DEBIAN_FRONTEND noninteractive
@@ -38,10 +38,9 @@ RUN cd TensorFlow && wget https://github.com/protocolbuffers/protobuf/releases/d
 RUN cd TensorFlow && unzip protoc-3.17.3-linux-x86_64.zip -d protobuf
 RUN cd TensorFlow/models/research && /TensorFlow/protobuf/bin/protoc object_detection/protos/*.proto --python_out=.
 RUN cd TensorFlow/models/research && cp object_detection/packages/tf2/setup.py .
-RUN cd TensorFlow/models/research && python -m pip install --use-feature=2020-resolver .
+RUN cd TensorFlow/models/research && python -m pip install .
 
 RUN pip install tqdm
 RUN pip install requests
-RUN pip install sklearn
 RUN pip install numba==0.50.0
 RUN pip install pytest-cov

.github/workflows/ci-deepspeech-v3.yml

@@ -25,7 +25,7 @@ jobs:
   test_deepspeech_v3_torch_2_1_1:
     name: PyTorchDeepSpeech v3 / PyTorch 2.1.1
     runs-on: ubuntu-latest
-    container: adversarialrobustnesstoolbox/art_testing_envs:deepspeech_v3_torch_2_1_1
+    container: bbuesser/art_testing_env_deepspeech_v3_torch_2_1_1:latest
     steps:
       - name: Checkout Repo
         uses: actions/checkout@v4

.github/workflows/ci-espresso.yml

@@ -25,7 +25,7 @@ jobs:
   test_espresso:
     name: PyTorchEspresso
     runs-on: ubuntu-latest
-    container: adversarialrobustnesstoolbox/art_testing_envs:espresso
+    container: bbuesser/art_testing_env_espresso:latest
     steps:
       - name: Checkout Repo
         uses: actions/checkout@v4

.github/workflows/ci-goturn.yml

@@ -25,7 +25,7 @@ jobs:
   test_pytorch_goturn:
     name: PyTorchGoturn
     runs-on: ubuntu-latest
-    container: adversarialrobustnesstoolbox/art_testing_envs:goturn
+    container: bbuesser/art_testing_env_goturn:latest
     steps:
       - name: Checkout Repo
         uses: actions/checkout@v4

.github/workflows/ci-legacy.yml

@@ -29,15 +29,15 @@ jobs:
       matrix:
         module: [attacks_1, attacks_2, estimators, defences, metrics, art]
         include:
-          - name: legacy (TensorFlow 2.14.0 Keras 2.14.0 PyTorch 1.13.1 scikit-learn 1.1.3 Python 3.9)
+          - name: legacy (TensorFlow 2.14.0 Keras 2.14.0 PyTorch 1.13.1 scikit-learn 1.6.1 Python 3.9)
             framework: legacy
             python: '3.10'
             tensorflow: 2.14.0
             keras: 2.14.0
             torch: 1.13.1+cpu
             torchvision: 0.14.1+cpu
             torchaudio: 0.13.1+cpu
-            scikit-learn: 1.1.3
+            scikit-learn: 1.6.1
 
     name: Run ${{ matrix.module }} ${{ matrix.name }} Tests
     steps:

.github/workflows/ci-scikit-learn.yml

@@ -28,13 +28,13 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - name: scikit-learn 1.3.2 (Python 3.10)
+          - name: scikit-learn 1.5.2 (Python 3.10)
             framework: scikitlearn
-            scikit-learn: 1.3.2
+            scikit-learn: 1.5.2
             python: '3.10'
-          - name: scikit-learn 1.4.0 (Python 3.10)
+          - name: scikit-learn 1.6.1 (Python 3.10)
             framework: scikitlearn
-            scikit-learn: 1.4.0
+            scikit-learn: 1.6.1
             python: '3.10'
 
     name: ${{ matrix.name }}
@@ -50,7 +50,7 @@ jobs:
           sudo apt-get update
           sudo apt-get -y -q install ffmpeg libavcodec-extra
           python -m pip install --upgrade pip setuptools wheel
-          pip install -r requirements_test.txt
+          pip install -q -r <(sed '/^scikit-learn/d' requirements_test.txt)
           pip install scikit-learn==${{ matrix.scikit-learn }}
           pip list
       - name: Run Tests

.github/workflows/ci-tfv2-faster-rcnn.yml

@@ -25,7 +25,7 @@ jobs:
   test_tf_v2_faster_rcnn:
     name: TensorFlowV2FasterRCNN
     runs-on: ubuntu-latest
-    container: adversarialrobustnesstoolbox/art_testing_envs:tfv2-faster-rcnn
+    container: bbuesser/art_testing_env_tfv2-faster-rcnn:latest
     steps:
       - name: Checkout Repo
         uses: actions/checkout@v4

requirements_test.txt

@@ -5,7 +5,7 @@
 numpy>=1.18.5,<1.27
 scipy==1.10.1
 matplotlib==3.7.1
-scikit-learn==1.4.1.post1
+scikit-learn==1.6.1
 six==1.17.0
 Pillow==11.1.0
 tqdm==4.67.1

tests/attacks/inference/attribute_inference/test_baseline.py

@@ -78,8 +78,8 @@ def transform_feature(x):
             baseline_inferred_test
         )
 
-        expected_train_acc = {"nn": 0.58, "rf": 0.98, "gb": 0.98, "lr": 0.77, "dt": 0.98, "knn": 0.86, "svm": 0.83}
-        expected_test_acc = {"nn": 0.62, "rf": 0.86, "gb": 0.82, "lr": 0.86, "dt": 0.84, "knn": 0.82, "svm": 0.93}
+        expected_train_acc = {"nn": 0.58, "rf": 0.98, "gb": 0.98, "lr": 0.77, "dt": 0.98, "knn": 0.84, "svm": 0.83}
+        expected_test_acc = {"nn": 0.62, "rf": 0.83, "gb": 0.79, "lr": 0.86, "dt": 0.82, "knn": 0.82, "svm": 0.93}
 
         assert expected_train_acc[model_type] <= baseline_train_acc
         assert expected_test_acc[model_type] <= baseline_test_acc
@@ -176,7 +176,7 @@ def transform_feature(x):
         )
 
         expected_train_acc = {"nn": 0.58, "rf": 0.98, "gb": 0.98, "lr": 0.77, "dt": 0.98, "knn": 0.85, "svm": 0.83}
-        expected_test_acc = {"nn": 0.62, "rf": 0.86, "gb": 0.82, "lr": 0.86, "dt": 0.8, "knn": 0.81, "svm": 0.93}
+        expected_test_acc = {"nn": 0.62, "rf": 0.83, "gb": 0.82, "lr": 0.86, "dt": 0.8, "knn": 0.81, "svm": 0.93}
 
         assert expected_train_acc[model_type] <= baseline_train_acc
         assert expected_test_acc[model_type] <= baseline_test_acc
@@ -228,7 +228,7 @@ def transform_feature(x):
         )
 
         expected_train_acc = {"nn": 0.58, "rf": 0.98, "gb": 0.98, "lr": 0.77, "dt": 0.98, "knn": 0.85, "svm": 0.83}
-        expected_test_acc = {"nn": 0.62, "rf": 0.88, "gb": 0.82, "lr": 0.86, "dt": 0.8, "knn": 0.81, "svm": 0.93}
+        expected_test_acc = {"nn": 0.62, "rf": 0.82, "gb": 0.8, "lr": 0.86, "dt": 0.8, "knn": 0.81, "svm": 0.93}
 
         assert expected_train_acc[model_type] <= baseline_train_acc
         assert expected_test_acc[model_type] <= baseline_test_acc
@@ -510,7 +510,7 @@ def transform_other_feature(x):
         )
 
         expected_train_acc = {"nn": 0.58, "rf": 0.94, "gb": 0.95, "lr": 0.8, "dt": 0.94, "knn": 0.87, "svm": 0.8}
-        expected_test_acc = {"nn": 0.62, "rf": 0.84, "gb": 0.84, "lr": 0.86, "dt": 0.82, "knn": 0.86, "svm": 0.88}
+        expected_test_acc = {"nn": 0.62, "rf": 0.82, "gb": 0.84, "lr": 0.86, "dt": 0.79, "knn": 0.86, "svm": 0.88}
 
         assert expected_train_acc[model_type] <= baseline_train_acc
         assert expected_test_acc[model_type] <= baseline_test_acc

tests/estimators/classification/test_scikitlearn.py

@@ -149,7 +149,7 @@ def test_type(self):
 
     def test_predict(self):
         y_predicted = self.classifier.predict(self.x_test_iris[0:1])
-        y_expected = np.asarray([[3.07686594e-16, 2.23540978e-02, 9.77645902e-01]])
+        y_expected = np.asarray([[0.25544427, 0.35099888, 0.39355685]])
         np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
 
     def test_save(self):
@@ -298,9 +298,9 @@ def test_class_gradient_none_1(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=None)
         grad_expected = [
             [
-                [-1.98021257, 1.35627806, -6.28222561, -2.60076308],
-                [-0.56322294, -0.70427608, -0.98874801, -0.67053026],
-                [0.6508137, 0.26377308, 1.54522324, 0.80972391],
+                [-1.97804999, 1.35988402, -6.28209543, -2.59983373],
+                [-0.56164223, -0.70078993, -0.98865014, -0.66950917],
+                [0.64962, 0.2610305, 1.54549754, 0.80910802],
             ]
         ]
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=3)
@@ -309,45 +309,60 @@ def test_class_gradient_none_2(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=None)
         grad_expected = [
             [
-                [-1.98021257, 1.35627806, -6.28222561, -2.60076308],
-                [-0.56322294, -0.70427608, -0.98874801, -0.67053026],
-                [0.6508137, 0.26377308, 1.54522324, 0.80972391],
+                [
+                    -1.97804999,
+                    1.35988402,
+                    -6.28209543,
+                    -2.59983397,
+                ],
+                [
+                    -0.56164235,
+                    -0.70078993,
+                    -0.98865038,
+                    -0.66950929,
+                ],
+                [
+                    0.64961988,
+                    0.26103044,
+                    1.5454973,
+                    0.8091079,
+                ],
             ],
             [
-                [-1.92227495, 1.34355378, -6.11815596, -2.52638054],
-                [-0.50528532, -0.71700042, -0.82467848, -0.59614766],
-                [0.70875132, 0.25104877, 1.70929277, 0.88410652],
+                [-1.9202075, 1.34698176, -6.1180439, -2.5255065],
+                [-0.50379974, -0.71369207, -0.82459873, -0.59518182],
+                [0.70746249, 0.24812828, 1.70954895, 0.88343531],
             ],
         ]
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=3)
 
     def test_class_gradient_int_1(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=1)
-        grad_expected = [[[-0.56317311, -0.70493763, -0.98908609, -0.67106276]]]
+        grad_expected = [[[-0.56164223, -0.70078993, -0.98865014, -0.66950917]]]
 
         for i_shape in range(4):
             self.assertAlmostEqual(grad_predicted[0, 0, i_shape], grad_expected[0][0][i_shape], 3)
 
     def test_class_gradient_int_2(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=1)
         grad_expected = [
-            [[-0.56317306, -0.70493776, -0.98908573, -0.67106259]],
-            [[-0.50522697, -0.71762568, -0.82497531, -0.5966416]],
+            [[-0.56164235, -0.70078993, -0.98865038, -0.66950929]],
+            [[-0.50379974, -0.71369207, -0.82459873, -0.59518182]],
         ]
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
 
     def test_class_gradient_list_1(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=[1])
-        grad_expected = [[[-0.56317311, -0.70493763, -0.98874801, -0.67106276]]]
+        grad_expected = [[[-0.56164223, -0.70078993, -0.98865014, -0.66950917]]]
 
         for i_shape in range(4):
             self.assertAlmostEqual(grad_predicted[0, 0, i_shape], grad_expected[0][0][i_shape], 3)
 
     def test_class_gradient_list_2(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=[1, 2])
         grad_expected = [
-            [[-0.56317306, -0.70493776, -0.98908573, -0.67106259]],
-            [[0.70866591, 0.25158876, 1.70947325, 0.88450021]],
+            [[-0.56164235, -0.70078993, -0.98865038, -0.66950929]],
+            [[0.70746249, 0.24812828, 1.70954895, 0.88343531]],
         ]
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=3)
 
@@ -362,7 +377,7 @@ def test_class_gradient_label_wrong_type(self):
 
     def test_loss_gradient(self):
         grad_predicted = self.classifier.loss_gradient(self.x_test_iris[0:1], self.y_test_iris[0:1])
-        grad_expected = np.asarray([[-0.21690657, -0.08809226, -0.51512082, -0.27002635]])
+        grad_expected = np.asarray([[-0.21654, -0.08701016, -0.51516586, -0.26970267]])
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
 
     def test_save(self):
@@ -541,36 +556,36 @@ def test_class_gradient_none(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=None)
         grad_expected = [
             [
-                [-0.34648966, 1.63777444, -3.51845999, -1.4609451],
-                [-0.11198028, -2.51565392, 0.71538245, -0.44830889],
-                [-0.38021886, -0.57562105, 3.59966607, 2.31772514],
+                [-0.34659522, 1.6376213, -3.51851979, -1.46078468],
+                [-0.1121541, -2.51552211, 0.71569165, -0.44883585],
+                [-0.380205, -0.57552204, 3.59972012, 2.31760663],
             ]
         ]
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
 
     def test_class_gradient_int_1(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=1)
-        grad_expected = [[[-0.11198028, -2.51565392, 0.71538245, -0.44830889]]]
+        grad_expected = [[[-0.1121541, -2.51552211, 0.71569165, -0.44883585]]]
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
 
     def test_class_gradient_int_2(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=1)
         grad_expected = [
-            [[-0.11198028, -2.51565392, 0.71538245, -0.44830889]],
-            [[-0.11198028, -2.51565392, 0.71538245, -0.44830889]],
+            [[-0.1121541, -2.51552211, 0.71569165, -0.44883585]],
+            [[-0.1121541, -2.51552211, 0.71569165, -0.44883585]],
         ]
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
 
     def test_class_gradient_list_1(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=[1])
-        grad_expected = [[[-0.11198028, -2.51565392, 0.71538245, -0.44830889]]]
+        grad_expected = [[[-0.1121541, -2.51552211, 0.71569165, -0.44883585]]]
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
 
     def test_class_gradient_list_2(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=[1, 2])
         grad_expected = [
-            [[-0.11198028, -2.51565392, 0.71538245, -0.44830889]],
-            [[-0.38021886, -0.57562105, 3.59966607, 2.31772514]],
+            [[-0.1121541, -2.51552211, 0.71569165, -0.44883585]],
+            [[-0.380205, -0.57552204, 3.59972012, 2.31760663]],
         ]
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
 

tests/utils.py

@@ -1748,10 +1748,10 @@ def get_tabular_classifier_scikit_list(clipped=False, model_list_names=None):
     )
 
     sklearn_version = list(map(int, sklearn.__version__.split(".")))
-    if sklearn_version[0] == 1 and sklearn_version[1] == 3:
-        suffix = "-eq-1.3.0"
-    elif sklearn_version[0] == 1 and sklearn_version[1] == 4:
-        suffix = "-eq-1.4.0"
+    if sklearn_version[0] == 1 and sklearn_version[1] == 5:
+        suffix = "-eq-1.5.2"
+    elif sklearn_version[0] == 1 and sklearn_version[1] == 6:
+        suffix = "-eq-1.6.1"
     else:
         suffix = ""
 

utils/resources/create_model_weights.py

@@ -154,7 +154,7 @@ def create_scikit_model_weights():
                 os.path.join(
                     os.path.dirname(os.path.dirname(__file__)),
                     "resources/models/scikit/",
-                    "scikit-" + model_name + "-iris-clipped-eq-1.4.0.pickle",
+                    "scikit-" + model_name + "-iris-clipped-eq-1.6.1.pickle",
                 ),
                 "wb",
             ),
@@ -168,7 +168,7 @@ def create_scikit_model_weights():
                 os.path.join(
                     os.path.dirname(os.path.dirname(__file__)),
                     "resources/models/scikit/",
-                    "scikit-" + model_name + "-iris-unclipped-eq-1.4.0.pickle",
+                    "scikit-" + model_name + "-iris-unclipped-eq-1.6.1.pickle",
                 ),
                 "wb",
             ),