def test_iterative_saliency(art_warning, fix_get_mnist_subset,
image_dl_estimator_for_attack):
try:
classifier = image_dl_estimator_for_attack(FastGradientMethod)
expected_values_axis_1 = {
"nb_perturbed_frames":
ExpectedValue(np.asarray([10, 1, 2, 12, 16, 1, 2, 7, 4, 11, 5]), 2)
}
expected_values_axis_2 = {
"nb_perturbed_frames":
ExpectedValue(np.asarray([11, 1, 2, 6, 14, 2, 2, 13, 4, 8, 4]), 2)
}
attacker = FastGradientMethod(classifier, eps=0.3, batch_size=128)
attack = FrameSaliencyAttack(classifier, attacker,
"iterative_saliency")
backend_check_adverse_frames(attack, fix_get_mnist_subset,
expected_values_axis_1)
# test with non-default frame index:
attack = FrameSaliencyAttack(classifier,
attacker,
"iterative_saliency",
frame_index=2)
backend_check_adverse_frames(attack, fix_get_mnist_subset,
expected_values_axis_2)
except ARTTestException as e:
art_warning(e)
def test_iterative_saliency(fix_get_mnist_subset,
image_dl_estimator_for_attack):
classifier_list = image_dl_estimator_for_attack(FastGradientMethod)
# TODO this if statement must be removed once we have a classifier for both image and tabular data
if classifier_list is None:
logging.warning(
"Couldn't perform this test because no classifier is defined")
return
expected_values_axis_1 = {
"nb_perturbed_frames":
ExpectedValue(np.asarray([10, 1, 2, 12, 16, 1, 2, 7, 4, 11, 5]), 2)
}
expected_values_axis_2 = {
"nb_perturbed_frames":
ExpectedValue(np.asarray([11, 1, 2, 6, 14, 2, 2, 13, 4, 8, 4]), 2)
}
for classifier in classifier_list:
attacker = FastGradientMethod(classifier, eps=0.3, batch_size=128)
attack = FrameSaliencyAttack(classifier, attacker,
"iterative_saliency")
backend_check_adverse_frames(attack, fix_get_mnist_subset,
expected_values_axis_1)
# test with non-default frame index:
attack = FrameSaliencyAttack(classifier,
attacker,
"iterative_saliency",
frame_index=2)
backend_check_adverse_frames(attack, fix_get_mnist_subset,
expected_values_axis_2)
