diff --git a/tests/attacks/evasion/test_patchfool_pytorch.py b/tests/attacks/evasion/test_patchfool_pytorch.py
new file mode 100644
index 0000000000..f334bbd05e
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+++ b/tests/attacks/evasion/test_patchfool_pytorch.py
@@ -0,0 +1,126 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2023
+#
+# 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 os
+import logging
+
+import numpy as np
+import pytest
+
+from art.attacks.evasion import PatchFoolPyTorch
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.estimators.classification.pytorch import PyTorchClassifier
+from art.estimators.estimator import BaseEstimator
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+@pytest.fixture()
+def get_pytorch_deit(get_default_cifar10_subset):
+
+    import cv2
+    import torch
+    from art.estimators.classification import PyTorchClassifier
+
+    MEAN = [0.485, 0.456, 0.406]
+    STD = [0.229, 0.224, 0.225]
+
+    model = torch.hub.load('facebookresearch/deit', 'deit_base_patch16_224', pretrained=True)
+    patch_size = 16
+
+    criterion = torch.nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+
+    classifier = PyTorchClassifier(
+        model=model,
+        loss=criterion,
+        optimizer=optimizer,
+        input_shape=(3, 224, 224),
+        nb_classes=10,
+        clip_values=(0.0, 1.0),
+        preprocessing=(MEAN, STD),
+    )
+    
+    return_nodes = [
+            "_model.blocks.0.attn.softmax",
+            "_model.blocks.1.attn.softmax",
+            "_model.blocks.2.attn.softmax",
+            "_model.blocks.3.attn.softmax",
+            "_model.blocks.4.attn.softmax",
+            "_model.blocks.5.attn.softmax",
+            "_model.blocks.6.attn.softmax",
+            "_model.blocks.7.attn.softmax",
+            "_model.blocks.8.attn.softmax",
+            "_model.blocks.9.attn.softmax",
+            "_model.blocks.10.attn.softmax",
+            "_model.blocks.11.attn.softmax",
+        ]
+
+    (_, _), (x_test_cifar10, y_test_cifar10) = get_default_cifar10_subset
+
+    x_test = cv2.resize(
+    x_test_cifar10[0].transpose((1, 2, 0)), dsize=(224, 224), interpolation=cv2.INTER_CUBIC
+    ).transpose((2, 0, 1))
+    x_test = np.expand_dims(x_test, axis=0)
+    x_test = np.repeat(x_test, repeats=2, axis=0)
+
+    return classifier, return_nodes, patch_size, x_test, y_test_cifar10
+
+@pytest.mark.skip_framework("tensorflow", "keras", "kerastf", "mxnet", "non_dl_frameworks")
+def test_generate_no_labels(art_warning, get_pytorch_deit):
+    try:
+
+        classifier, return_nodes, patch_size, x_test, _ = get_pytorch_deit
+
+        attack = PatchFoolPyTorch(estimator=classifier, 
+                        attention_nodes=return_nodes, 
+                        patch_size=patch_size, 
+                        max_iter=10, 
+                        random_start=False)
+                        
+        x_test_adv = attack.generate(x=x_test)
+
+        assert np.mean(x_test) == pytest.approx(0.4250448, 0.01)
+        assert np.mean(np.abs(x_test_adv - x_test)) != 0.0
+
+    except ARTTestException as e:
+        art_warning(e)
+
+@pytest.mark.skip_framework("tensorflow", "keras", "kerastf", "mxnet", "non_dl_frameworks")
+def test_check_params(art_warning, get_pytorch_deit):
+    try:
+        classifier, return_nodes, patch_size, _, _ = get_pytorch_deit
+
+        with pytest.raises(TypeError):
+            _ = PatchFoolPyTorch(classifier, attention_nodes=0, patch_size=patch_size)
+
+        with pytest.raises(ValueError):
+            _ = PatchFoolPyTorch(classifier, attention_nodes=return_nodes, patch_size=-1)
+
+        with pytest.raises(ValueError):
+            _ = PatchFoolPyTorch(classifier, attention_nodes=return_nodes, patch_size=patch_size, max_iter=-1)
+
+        with pytest.raises(ValueError):
+            _ = PatchFoolPyTorch(classifier, attention_nodes=return_nodes, patch_size=patch_size, batch_size=0)
+
+        with pytest.raises(ValueError):
+            _ = PatchFoolPyTorch(classifier, attention_nodes=return_nodes, patch_size=patch_size, patch_layer=-1)
+
+    except ARTTestException as e:
+        art_warning(e)