def test_4_pytorch(self):
"""
Third test with the PyTorchClassifier.
:return:
"""
ptc = get_image_classifier_pt(from_logits=True)
x_train = np.reshape(self.x_train_mnist, (self.n_train, 1, 28, 28)).astype(np.float32)
attack_ap = AdversarialPatch(
ptc,
rotation_max=0.5,
scale_min=0.4,
scale_max=0.41,
learning_rate=5.0,
batch_size=10,
max_iter=5,
verbose=False,
)
target = np.zeros(self.x_train_mnist.shape[0])
patch_adv, _ = attack_ap.generate(x_train, target)
self.assertAlmostEqual(patch_adv[0, 8, 8], 0.5, delta=0.05)
self.assertAlmostEqual(patch_adv[0, 14, 14], 0.5, delta=0.05)
self.assertAlmostEqual(float(np.sum(patch_adv)), 371.88014772999827, delta=4.0)
mask = np.ones((1, 28, 28)).astype(bool)
attack_ap.apply_patch(x=x_train, scale=0.1, mask=mask)
attack_ap.reset_patch(initial_patch_value=None)
attack_ap.reset_patch(initial_patch_value=1.0)
attack_ap.reset_patch(initial_patch_value=patch_adv)
with self.assertRaises(ValueError):
attack_ap.reset_patch(initial_patch_value=np.array([1, 2, 3]))
# Numpy
attack_ap = AdversarialPatchNumpy(
ptc,
rotation_max=0.5,
scale_min=0.4,
scale_max=0.41,
learning_rate=5.0,
batch_size=10,
max_iter=5,
verbose=False,
)
target = np.zeros(self.x_train_mnist.shape[0])
patch_adv, _ = attack_ap.generate(x_train, target)
self.assertAlmostEqual(patch_adv[0, 8, 8], 0.6715167, delta=0.05)
self.assertAlmostEqual(patch_adv[0, 14, 14], 0.6292826, delta=0.05)
self.assertAlmostEqual(float(np.sum(patch_adv)), 424.31439208984375, delta=4.0)
mask = np.ones((1, 28, 28)).astype(bool)
attack_ap.apply_patch(x=x_train, scale=0.1, mask=mask)
attack_ap.reset_patch(initial_patch_value=None)
attack_ap.reset_patch(initial_patch_value=1.0)
attack_ap.reset_patch(initial_patch_value=patch_adv)
with self.assertRaises(ValueError):
attack_ap.reset_patch(initial_patch_value=np.array([1, 2, 3]))
def test_3_tensorflow_v2_framework(self):
"""
First test with the TensorFlowClassifier.
:return:
"""
tfc, _ = get_image_classifier_tf(from_logits=True)
attack_ap = AdversarialPatch(
tfc,
rotation_max=0.5,
scale_min=0.4,
scale_max=0.41,
learning_rate=5.0,
batch_size=10,
max_iter=10,
patch_shape=(28, 28, 1),
verbose=False,
)
target = np.zeros(self.x_train_mnist.shape[0])
patch_adv, _ = attack_ap.generate(self.x_train_mnist, target, shuffle=False)
self.assertAlmostEqual(patch_adv[8, 8, 0], 1.0, delta=0.05)
self.assertAlmostEqual(patch_adv[14, 14, 0], 0.0, delta=0.05)
self.assertAlmostEqual(float(np.sum(patch_adv)), 377.415771484375, delta=1.0)
# insert_transformed_patch
x_out = attack_ap.insert_transformed_patch(
self.x_train_mnist[0], np.ones((14, 14, 1)), np.asarray([[2, 13], [2, 18], [12, 22], [8, 13]])
)
x_out_expexted = np.array(
[
0.0,
0.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.84313726,
0.0,
0.0,
0.0,
0.0,
0.1764706,
0.7294118,
0.99215686,
0.99215686,
0.5882353,
0.10588235,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
dtype=np.float32,
)
np.testing.assert_almost_equal(x_out[15, :, 0], x_out_expexted, decimal=3)
mask = np.ones((1, 28, 28)).astype(bool)
attack_ap.apply_patch(x=self.x_train_mnist, scale=0.1, mask=mask)
attack_ap.reset_patch(initial_patch_value=None)
attack_ap.reset_patch(initial_patch_value=1.0)
attack_ap.reset_patch(initial_patch_value=patch_adv)