def test_fgsm_defences(fix_get_mnist_subset, image_dl_estimator, device_type):
clip_values = (0, 1)
smooth_3x3 = SpatialSmoothingPyTorch(window_size=3,
channels_first=True,
device_type=device_type)
smooth_5x5 = SpatialSmoothingPyTorch(window_size=5,
channels_first=True,
device_type=device_type)
smooth_7x7 = SpatialSmoothingPyTorch(window_size=7,
channels_first=True,
device_type=device_type)
classifier_, _ = image_dl_estimator(one_classifier=True)
criterion = nn.CrossEntropyLoss()
classifier = PyTorchClassifier(
clip_values=clip_values,
model=classifier_.model,
preprocessing_defences=[smooth_3x3, smooth_5x5, smooth_7x7],
loss=criterion,
input_shape=(1, 28, 28),
nb_classes=10,
device_type=device_type,
)
assert len(classifier.preprocessing_defences) == 3
attack = FastGradientMethod(classifier, eps=1, batch_size=128)
backend_test_defended_images(attack, fix_get_mnist_subset)
def test_spatial_smoothing_estimate_gradient(art_warning):
try:
test_input = np.array([[[[1], [2]], [[3], [4]]]]).astype(float)
test_output = np.array([[[[2], [2]], [[2], [2]]]]).astype(float)
spatial_smoothing = SpatialSmoothingPyTorch(channels_first=False,
window_size=2)
test_gradients = spatial_smoothing.estimate_gradient(
x=test_input, grad=np.ones_like(test_output).astype(float))
assert test_gradients.shape == test_input.shape
except ARTTestException as e:
art_warning(e)
def test_defences_chaining(art_warning, get_default_mnist_subset, image_dl_estimator, device_type):
try:
smooth_3x3 = SpatialSmoothingPyTorch(window_size=3, channels_first=True, device_type=device_type)
smooth_5x5 = SpatialSmoothingPyTorch(window_size=5, channels_first=True, device_type=device_type)
smooth_7x7 = SpatialSmoothingPyTorch(window_size=7, channels_first=True, device_type=device_type)
preprocessing_defences = [smooth_3x3, smooth_5x5, smooth_7x7]
_test_preprocessing_defences_forward(
get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
)
_test_preprocessing_defences_backward(
get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
)
except ARTTestException as e:
art_warning(e)
def test_spatial_smoothing_image_data(self, image_batch, channels_first,
window_size):
test_input, test_output = image_batch
spatial_smoothing = SpatialSmoothingPyTorch(
channels_first=channels_first, window_size=window_size)
assert_array_equal(spatial_smoothing(test_input)[0], test_output)
def test_triple_clip_values_error(art_warning):
try:
exc_msg = "'clip_values' should be a tuple of 2 floats or arrays containing the allowed data range."
with pytest.raises(ValueError, match=exc_msg):
SpatialSmoothingPyTorch(clip_values=(0, 1, 2))
except ARTTestException as e:
art_warning(e)
def test_non_spatial_data_error(self, tabular_batch):
test_input = tabular_batch
spatial_smoothing = SpatialSmoothingPyTorch(channels_first=True)
exc_msg = "Unrecognized input dimension. Spatial smoothing can only be applied to image and video data."
with pytest.raises(ValueError, match=exc_msg):
spatial_smoothing(test_input)
def test_relation_clip_values_error(art_warning):
try:
exc_msg = "Invalid 'clip_values': min >= max."
with pytest.raises(ValueError, match=exc_msg):
SpatialSmoothingPyTorch(clip_values=(1, 0))
except ARTTestException as e:
art_warning(e)
def test_spatial_smoothing_median_filter_call_expected_behavior(self):
test_input = np.array([[[[1, 2], [3, 4]]]])
test_output = np.array([[[[2, 2], [2, 2]]]])
spatial_smoothing = SpatialSmoothingPyTorch(channels_first=True,
window_size=2)
assert_array_equal(spatial_smoothing(test_input)[0], test_output)
def test_window_size_error(art_warning):
try:
exc_msg = "Sliding window size must be a positive integer."
with pytest.raises(ValueError, match=exc_msg):
SpatialSmoothingPyTorch(window_size=0)
except ARTTestException as e:
art_warning(e)
def test_spatial_smoothing_image_data(art_warning, image_batch, channels_first,
window_size):
try:
test_input, test_output = image_batch
spatial_smoothing = SpatialSmoothingPyTorch(
channels_first=channels_first, window_size=window_size)
assert_array_equal(spatial_smoothing(test_input)[0], test_output)
except ARTTestException as e:
art_warning(e)
def test_spatial_smoothing_median_filter_call_expected_behavior(art_warning):
try:
test_input = np.array([[[[1, 2], [3, 4]]]])
test_output = np.array([[[[2, 2], [2, 2]]]])
spatial_smoothing = SpatialSmoothingPyTorch(channels_first=True,
window_size=2)
assert_array_equal(spatial_smoothing(test_input)[0], test_output)
except ARTTestException as e:
art_warning(e)
def test_defence_pytorch(get_default_mnist_subset, image_dl_estimator,
device_type):
smooth_3x3 = SpatialSmoothingPyTorch(window_size=3,
channels_first=True,
device_type=device_type)
preprocessing_defences = [smooth_3x3]
_test_preprocessing_defences_forward(get_default_mnist_subset,
image_dl_estimator, device_type,
preprocessing_defences)
_test_preprocessing_defences_backward(get_default_mnist_subset,
image_dl_estimator, device_type,
preprocessing_defences)
def test_defences_pytorch_and_nonpytorch(art_warning, get_default_mnist_subset, image_dl_estimator, device_type):
try:
smooth_3x3_nonpth = SpatialSmoothing(window_size=3, channels_first=True)
smooth_3x3_pth = SpatialSmoothingPyTorch(window_size=3, channels_first=True, device_type=device_type)
preprocessing_defences = [smooth_3x3_nonpth, smooth_3x3_pth]
_test_preprocessing_defences_forward(
get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
)
_test_preprocessing_defences_backward(
get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
)
except ARTTestException as e:
art_warning(e)
def test_window_size_error(self):
exc_msg = "Sliding window size must be a positive integer."
with pytest.raises(ValueError, match=exc_msg):
SpatialSmoothingPyTorch(window_size=0)
def test_triple_clip_values_error(self):
exc_msg = "'clip_values' should be a tuple of 2 floats or arrays containing the allowed data range."
with pytest.raises(ValueError, match=exc_msg):
SpatialSmoothingPyTorch(clip_values=(0, 1, 2))
def test_spatial_smoothing_video_data(self, video_batch, channels_first):
test_input, test_output = video_batch
spatial_smoothing = SpatialSmoothingPyTorch(
channels_first=channels_first, window_size=2)
assert_array_equal(spatial_smoothing(test_input)[0], test_output)
def test_relation_clip_values_error(self):
exc_msg = "Invalid 'clip_values': min >= max."
with pytest.raises(ValueError, match=exc_msg):
SpatialSmoothingPyTorch(clip_values=(1, 0))