def test_fgsm_defences(fix_get_mnist_subset, image_dl_estimator,
is_tf_version_2):
if is_tf_version_2:
clip_values = (0, 1)
smooth_3x3 = SpatialSmoothingTensorFlowV2(window_size=3,
channels_first=False)
smooth_5x5 = SpatialSmoothingTensorFlowV2(window_size=5,
channels_first=False)
smooth_7x7 = SpatialSmoothingTensorFlowV2(window_size=7,
channels_first=False)
classifier_, _ = image_dl_estimator(one_classifier=True)
loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
classifier = TensorFlowV2Classifier(
clip_values=clip_values,
model=classifier_.model,
preprocessing_defences=[smooth_3x3, smooth_5x5, smooth_7x7],
loss_object=loss_object,
input_shape=(28, 28, 1),
nb_classes=10,
)
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_video_data(art_warning, video_batch, channels_first):
try:
test_input, test_output = video_batch
if channels_first:
exc_msg = "Only channels last input data is supported"
with pytest.raises(ValueError, match=exc_msg):
_ = SpatialSmoothingTensorFlowV2(channels_first=channels_first, window_size=2)
else:
spatial_smoothing = SpatialSmoothingTensorFlowV2(channels_first=channels_first, window_size=2)
assert_array_equal(spatial_smoothing(test_input)[0], test_output)
except ARTTestException as e:
art_warning(e)
def test_defences_chaining(art_warning, get_default_mnist_subset, image_dl_estimator):
try:
smooth_3x3 = SpatialSmoothingTensorFlowV2(window_size=3, channels_first=False)
smooth_5x5 = SpatialSmoothingTensorFlowV2(window_size=5, channels_first=False)
smooth_7x7 = SpatialSmoothingTensorFlowV2(window_size=7, channels_first=False)
preprocessing_defences = [smooth_3x3, smooth_5x5, smooth_7x7]
device_type = None
_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_video_data(self, video_batch, channels_first,
is_tf_version_2):
if is_tf_version_2:
test_input, test_output = video_batch
if channels_first:
exc_msg = "Only channels last input data is supported"
with pytest.raises(ValueError, match=exc_msg):
_ = SpatialSmoothingTensorFlowV2(
channels_first=channels_first, window_size=2)
else:
spatial_smoothing = SpatialSmoothingTensorFlowV2(
channels_first=channels_first, window_size=2)
assert_array_equal(
spatial_smoothing(test_input)[0], test_output)
def test_relation_clip_values_error(art_warning):
try:
exc_msg = "Invalid 'clip_values': min >= max."
with pytest.raises(ValueError, match=exc_msg):
SpatialSmoothingTensorFlowV2(clip_values=(1, 0))
except ARTTestException as e:
art_warning(e)
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):
SpatialSmoothingTensorFlowV2(clip_values=(0, 1, 2))
except ARTTestException as e:
art_warning(e)
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):
SpatialSmoothingTensorFlowV2(window_size=0)
except ARTTestException as e:
art_warning(e)
def test_defences_chaining(get_default_mnist_subset, image_dl_estimator,
is_tf_version_2):
if is_tf_version_2:
smooth_3x3 = SpatialSmoothingTensorFlowV2(window_size=3,
channels_first=False)
smooth_5x5 = SpatialSmoothingTensorFlowV2(window_size=5,
channels_first=False)
smooth_7x7 = SpatialSmoothingTensorFlowV2(window_size=7,
channels_first=False)
preprocessing_defences = [smooth_3x3, smooth_5x5, smooth_7x7]
device_type = None
_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_spatial_smoothing_median_filter_call(self, is_tf_version_2):
if is_tf_version_2:
test_input = np.array([[[[1], [2]], [[3], [4]]]])
test_output = np.array([[[[1], [2]], [[3], [3]]]])
spatial_smoothing = SpatialSmoothingTensorFlowV2(
channels_first=False, window_size=2)
assert_array_equal(spatial_smoothing(test_input)[0], test_output)
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 = SpatialSmoothingTensorFlowV2(channels_first=False, window_size=2)
assert_array_equal(spatial_smoothing(test_input)[0], test_output)
except ARTTestException as e:
art_warning(e)
def test_non_spatial_data_error(self, tabular_batch, is_tf_version_2):
if is_tf_version_2:
test_input = tabular_batch
spatial_smoothing = SpatialSmoothingTensorFlowV2(
channels_first=False)
exc_msg = "Unrecognized input dimension. Spatial smoothing can only be applied to image"
with pytest.raises(ValueError, match=exc_msg):
spatial_smoothing(test_input)
def test_non_spatial_data_error(art_warning, tabular_batch):
try:
test_input = tabular_batch
spatial_smoothing = SpatialSmoothingTensorFlowV2(channels_first=False)
exc_msg = "Unrecognized input dimension. Spatial smoothing can only be applied to image"
with pytest.raises(ValueError, match=exc_msg):
spatial_smoothing(test_input)
except ARTTestException as e:
art_warning(e)
def test_spatial_smoothing_estimate_gradient(art_warning):
try:
test_input = np.array([[[[1], [2]], [[3], [4]]]])
test_output = np.array([[[[2], [2]], [[2], [2]]]])
spatial_smoothing = SpatialSmoothingTensorFlowV2(channels_first=False,
window_size=2)
test_gradients = spatial_smoothing.estimate_gradient(
x=test_input, grad=np.ones_like(test_output))
assert test_gradients.shape == test_input.shape
except ARTTestException as e:
art_warning(e)
def test_defences_tensorflow_and_nontensorflow(art_warning, get_default_mnist_subset, image_dl_estimator, device_type):
try:
smooth_3x3_nonpth = SpatialSmoothing(window_size=3, channels_first=False)
smooth_3x3_pth = SpatialSmoothingTensorFlowV2(window_size=3, channels_first=False)
preprocessing_defences = [smooth_3x3_nonpth, smooth_3x3_pth]
device_type = None
_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_defences_tensorflow_and_nontensorflow(get_default_mnist_subset,
image_dl_estimator, device_type,
is_tf_version_2):
if is_tf_version_2:
smooth_3x3_nonpth = SpatialSmoothing(window_size=3,
channels_first=False)
smooth_3x3_pth = SpatialSmoothingTensorFlowV2(window_size=3,
channels_first=False)
preprocessing_defences = [smooth_3x3_nonpth, smooth_3x3_pth]
device_type = None
_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_window_size_error(self, is_tf_version_2):
if is_tf_version_2:
exc_msg = "Sliding window size must be a positive integer."
with pytest.raises(ValueError, match=exc_msg):
SpatialSmoothingTensorFlowV2(window_size=0)
def test_relation_clip_values_error(self, is_tf_version_2):
if is_tf_version_2:
exc_msg = "Invalid 'clip_values': min >= max."
with pytest.raises(ValueError, match=exc_msg):
SpatialSmoothingTensorFlowV2(clip_values=(1, 0))
def test_triple_clip_values_error(self, is_tf_version_2):
if is_tf_version_2:
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):
SpatialSmoothingTensorFlowV2(clip_values=(0, 1, 2))