Ejemplo n.º 1
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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)
Ejemplo n.º 3
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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)
Ejemplo n.º 4
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    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)
Ejemplo n.º 8
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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)
Ejemplo n.º 9
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    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)
Ejemplo n.º 11
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    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)
Ejemplo n.º 13
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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)
Ejemplo n.º 14
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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)
Ejemplo n.º 15
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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)
Ejemplo n.º 16
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 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)
Ejemplo n.º 17
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 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))
Ejemplo n.º 18
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 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))