b0aa63acadaa721d8b69f2b060048aad874522e5,tests/attacks/evasion/test_fast_gradient.py,,test_classifier_defended_images,#Any#Any#,108

Before Change

        return

    for classifier in defended_image_classifier_list:
        if FastGradientMethod.is_valid_classifier_type(classifier) is False:
            continue

        attack = FastGradientMethod(classifier, eps=1, batch_size=128)
        x_train_adv = attack.generate(x_train_mnist)

        utils_test.check_adverse_example_x(x_train_adv, x_train_mnist)

After Change

def test_classifier_defended_images(fix_get_mnist_subset, new_image_classifier_list):
    (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
    classifier_list = new_image_classifier_list(FastGradientMethod, defended=True)
    // 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

Instances