Adding compatibility check in GeoDA for neural network-based classifiers

Signed-off-by: Vishal Gawade <vishalgawade311@gmail.com>

Author: VishalGawade1

art/attacks/evasion/geometric_decision_based_attack.py

@@ -94,6 +94,13 @@ def __init__(
         """
         super().__init__(estimator=estimator)
 
+        # **Compatibility Checks**
+        if not hasattr(estimator, "input_shape") or not hasattr(estimator, "channels_first"):
+            raise ValueError(
+                f"GeoDA is incompatible with {type(estimator)}. "
+                "Please use a neural network-based classifier."
+            )
+
         self.batch_size = batch_size
         self.norm = norm
         self.sub_dim = sub_dim
@@ -102,16 +109,18 @@ def __init__(
         self.lambda_param = lambda_param
         self.sigma = sigma
         self._targeted = False
-
         self.verbose = verbose
+
         self._check_params()
 
         self.sub_basis: np.ndarray
         self.nb_calls = 0
         self.clip_min = 0.0
         self.clip_max = 0.0
+
         if self.estimator.input_shape is None:  # pragma: no cover
-            raise ValueError("The `input_shape` of the is required but None.")
+            raise ValueError("The `input_shape` of the estimator is required but None.")
+
         self.nb_channels = (
             self.estimator.input_shape[0] if self.estimator.channels_first else self.estimator.input_shape[2]
         )
@@ -450,4 +459,4 @@ def _check_params(self) -> None:
         #     raise ValueError("The argument `targeted` has to be of type bool.")
 
         if not isinstance(self.verbose, bool):
-            raise ValueError("The argument `verbose` has to be of type bool.")
+            raise ValueError("The argument `verbose` has to be of type bool.")

requirements_test.txt

@@ -60,7 +60,7 @@ pycodestyle==2.12.1
 black==24.8.0
 ruff==0.5.5
 types-six==1.16.21.9
-types-PyYAML==6.0.12.20240724
+types-PyYAML==6.0.12.20240917
 types-setuptools==71.1.0.20240726
 
 # other

tests/attacks/evasion/test_geoda_incompatibility.py

@@ -0,0 +1,28 @@
+# test_geoda_incompatibility.py
+
+import pytest
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from art.estimators.classification import SklearnClassifier
+from art.attacks.evasion import GeoDA
+
+
+def test_geoda_with_random_forest():
+    # Load the Iris dataset
+    data = load_iris()
+    X_train, X_test, y_train, y_test = train_test_split(
+        data.data, data.target, test_size=0.2, random_state=42
+    )
+
+    # Train a RandomForestClassifier
+    model = RandomForestClassifier()
+    model.fit(X_train, y_train)
+
+    # Wrap the model with ART's SklearnClassifier
+    classifier = SklearnClassifier(model=model)
+
+    # Expect GeoDA to raise ValueError when used with RandomForestClassifier
+    with pytest.raises(ValueError, match="GeoDA is incompatible with"):
+        attack = GeoDA(classifier)
+        attack.generate(X_test)