def test_minimal_perturbations_images(fix_get_mnist_subset,
get_image_classifier_list_for_attack):
classifier_list = get_image_classifier_list_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
for classifier in classifier_list:
attack = FastGradientMethod(classifier, eps=1.0, batch_size=11)
attack_params = {"minimal": True, "eps_step": 0.1, "eps": 5.0}
attack.set_params(**attack_params)
expected_values = {
"x_test_mean":
ExpectedValue(0.03896513, 0.01),
"x_test_min":
ExpectedValue(-0.30000000, 0.00001),
"x_test_max":
ExpectedValue(0.30000000, 0.00001),
"y_test_pred_adv_expected":
ExpectedValue(np.asarray([4, 2, 4, 7, 0, 4, 7, 2, 0, 7, 0]), 2),
}
backend_check_adverse_values(attack, fix_get_mnist_subset,
expected_values)
def test_minimal_perturbations_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
try:
classifier = image_dl_estimator_for_attack(FastGradientMethod)
expected_values = {
"x_test_mean": ExpectedValue(0.03896513, 0.01),
"x_test_min": ExpectedValue(-0.30000000, 0.00001),
"x_test_max": ExpectedValue(0.30000000, 0.00001),
"y_test_pred_adv_expected": ExpectedValue(np.asarray([4, 2, 4, 7, 0, 4, 7, 2, 0, 7, 0]), 2),
}
attack = FastGradientMethod(classifier, eps=1.0, batch_size=11)
# Test eps of float type
attack_params = {"minimal": True, "eps_step": 0.1, "eps": 5.0}
attack.set_params(**attack_params)
backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
# Test eps of array type 1
(_, _, x_test_mnist, _) = fix_get_mnist_subset
eps = np.ones(shape=x_test_mnist.shape) * 5.0
eps_step = np.ones_like(eps) * 0.1
attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
attack.set_params(**attack_params)
backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
# Test eps of array type 2
eps = np.ones(shape=x_test_mnist.shape[1:]) * 5.0
eps_step = np.ones_like(eps) * 0.1
attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
attack.set_params(**attack_params)
backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
# Test eps of array type 3
eps = np.ones(shape=x_test_mnist.shape[2:]) * 5.0
eps_step = np.ones_like(eps) * 0.1
attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
attack.set_params(**attack_params)
backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
# Test eps of array type 4
eps = np.ones(shape=x_test_mnist.shape[3:]) * 5.0
eps_step = np.ones_like(eps) * 0.1
attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
attack.set_params(**attack_params)
backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
except ARTTestException as e:
art_warning(e)
def test_targeted_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
try:
classifier = image_dl_estimator_for_attack(FastGradientMethod)
attack = FastGradientMethod(classifier, eps=1.0, targeted=True)
attack_params = {"minimal": True, "eps_step": 0.01, "eps": 1.0}
attack.set_params(**attack_params)
backend_targeted_images(attack, fix_get_mnist_subset)
except ARTTestException as e:
art_warning(e)
def test_targeted_images(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
for classifier in classifier_list:
attack = FastGradientMethod(classifier, eps=1.0, targeted=True)
attack_params = {"minimal": True, "eps_step": 0.01, "eps": 1.0}
attack.set_params(**attack_params)
backend_targeted_images(attack, fix_get_mnist_subset)
