def test_sub_noise(art_warning, image_dl_estimator, fix_get_mnist_subset):
try:
(x_train_mnist, y_train_mnist, x_test_mnist,
y_test_mnist) = fix_get_mnist_subset
classifier, _ = image_dl_estimator(from_logits=True)
attack = GeoDA(estimator=classifier,
sub_dim=5,
max_iter=4000,
verbose=False)
c = attack._sub_noise(num_noises=64, basis=np.ones((28 * 28, 25)))
assert c[0].shape == x_train_mnist[0].shape
except ARTTestException as e:
art_warning(e)
def test_is_adversarial(art_warning, image_dl_estimator, fix_get_mnist_subset):
try:
(x_train_mnist, y_train_mnist, x_test_mnist,
y_test_mnist) = fix_get_mnist_subset
classifier, _ = image_dl_estimator(from_logits=True)
attack = GeoDA(estimator=classifier,
sub_dim=5,
max_iter=4000,
verbose=False)
assert not attack._is_adversarial(x_adv=x_test_mnist[[0]],
y_true=y_test_mnist[[0]])
except ARTTestException as e:
art_warning(e)
def test_generate(art_warning, fix_get_mnist_subset, image_dl_estimator):
try:
(x_train_mnist, y_train_mnist, x_test_mnist,
y_test_mnist) = fix_get_mnist_subset
classifier, _ = image_dl_estimator(from_logits=True)
attack = GeoDA(estimator=classifier,
sub_dim=5,
max_iter=400,
verbose=False)
x_train_mnist_adv = attack.generate(x=x_train_mnist, y=y_train_mnist)
assert np.mean(np.abs(x_train_mnist_adv -
x_train_mnist)) == pytest.approx(0.057965796,
abs=0.01)
except ARTTestException as e:
art_warning(e)
def test_opt_query_iteration(art_warning, image_dl_estimator):
try:
classifier, _ = image_dl_estimator(from_logits=True)
attack = GeoDA(estimator=classifier,
sub_dim=5,
max_iter=4000,
verbose=False)
opt_q, var_t = attack._opt_query_iteration(var_nq=3800,
var_t=8,
lambda_param=0.6)
opt_q_expected = [75, 106, 149, 210, 295, 414, 582, 818, 1150]
var_t_expected = 9
assert opt_q == opt_q_expected
assert var_t == var_t_expected
except ARTTestException as e:
art_warning(e)
def test_generate_2d_dct_basis(art_warning, image_dl_estimator):
try:
sub_dim = 5
res = 224
classifier, _ = image_dl_estimator(from_logits=True)
attack = GeoDA(estimator=classifier,
sub_dim=5,
max_iter=4000,
verbose=False)
dct = attack._generate_2d_dct_basis(sub_dim=sub_dim, res=res)
assert dct.shape == (res * res, sub_dim * sub_dim)
dct_50000 = np.array([[
0.00446429,
-0.0063133,
0.00631283,
-0.00631206,
0.00631097,
0.00490833,
-0.00694126,
0.00694075,
-0.00693989,
0.0069387,
0.00131841,
-0.00186447,
0.00186434,
-0.00186411,
0.00186379,
-0.00285836,
0.00404223,
-0.00404193,
0.00404143,
-0.00404074,
-0.00576281,
0.00814965,
-0.00814905,
0.00814805,
-0.00814664,
]])
np.testing.assert_almost_equal(dct[50000], dct_50000)
except ARTTestException as e:
art_warning(e)
def test_find_random_adversarial(art_warning, image_dl_estimator,
fix_get_mnist_subset):
try:
(x_train_mnist, y_train_mnist, x_test_mnist,
y_test_mnist) = fix_get_mnist_subset
classifier, _ = image_dl_estimator(from_logits=True)
attack = GeoDA(estimator=classifier,
sub_dim=5,
max_iter=4000,
verbose=False)
x_adv = attack._find_random_adversarial(x=x_test_mnist[[0]],
y=y_test_mnist[[0]])
assert np.argmax(classifier.predict(x_adv)) != np.argmax(
y_test_mnist[[0]])
except ARTTestException as e:
art_warning(e)
def test_check_params(art_warning, image_dl_estimator_for_attack):
try:
classifier = image_dl_estimator_for_attack(GeoDA, from_logits=True)
with pytest.raises(ValueError):
_ = GeoDA(classifier, batch_size=1.0)
with pytest.raises(ValueError):
_ = GeoDA(classifier, batch_size=-1)
with pytest.raises(ValueError):
_ = GeoDA(classifier, norm=0)
with pytest.raises(ValueError):
_ = GeoDA(classifier, sub_dim=1.0)
with pytest.raises(ValueError):
_ = GeoDA(classifier, sub_dim=-1)
with pytest.raises(ValueError):
_ = GeoDA(classifier, max_iter=1.0)
with pytest.raises(ValueError):
_ = GeoDA(classifier, max_iter=-1)
with pytest.raises(ValueError):
_ = GeoDA(classifier, max_iter=1.0)
with pytest.raises(ValueError):
_ = GeoDA(classifier, max_iter=-1)
with pytest.raises(ValueError):
_ = GeoDA(classifier, bin_search_tol="1")
with pytest.raises(ValueError):
_ = GeoDA(classifier, bin_search_tol=-1.0)
with pytest.raises(ValueError):
_ = GeoDA(classifier, lambda_param=1)
with pytest.raises(ValueError):
_ = GeoDA(classifier, lambda_param=-1.0)
with pytest.raises(ValueError):
_ = GeoDA(classifier, sigma=1)
with pytest.raises(ValueError):
_ = GeoDA(classifier, sigma=-1.0)
# with pytest.raises(ValueError):
# _ = GeoDA(classifier, targeted="true")
with pytest.raises(ValueError):
_ = GeoDA(classifier, verbose="true")
except ARTTestException as e:
art_warning(e)