def test_attack_convergence(bn_adversarial):
adv = bn_adversarial
attack1 = DeepFoolAttack()
attack1(adv)
attack2 = BoundaryAttack()
attack2(adv, iterations=5000, verbose=True)
# should converge
assert adv.perturbed is not None
assert adv.distance.value < np.inf
def test_attack_continue(bn_adversarial):
adv = bn_adversarial
attack1 = BlendedUniformNoiseAttack()
attack1(adv)
d1 = adv.distance.value
attack2 = BoundaryAttack()
attack2(adv, iterations=200, verbose=True)
assert adv.perturbed is not None
assert adv.distance.value < np.inf
assert adv.distance.value < d1
def test_attack_parameters3(bn_adversarial):
adv = bn_adversarial
attack = BoundaryAttack()
o = adv.unperturbed
np.random.seed(2)
starting_point = np.random.uniform(0, 1, size=o.shape).astype(o.dtype)
attack(
adv,
iterations=200,
starting_point=starting_point,
log_every_n_steps=2,
tune_batch_size=30,
threaded_rnd=False,
threaded_gen=False,
verbose=True,
)
assert adv.perturbed is not None
assert adv.distance.value < np.inf
def test_attack_impossible(bn_impossible):
adv = bn_impossible
attack = BoundaryAttack()
attack(adv, iterations=200, verbose=True)
assert adv.perturbed is None
assert adv.distance.value == np.inf
def test_attack_gl(gl_bn_adversarial):
adv = gl_bn_adversarial
attack = BoundaryAttack()
attack(adv, iterations=200, verbose=True)
assert adv.perturbed is not None
assert adv.distance.value < np.inf
def test_attack_parameters2(bn_adversarial):
adv = bn_adversarial
attack = BoundaryAttack()
attack(adv, iterations=200, alternative_generator=True, verbose=True)
assert adv.perturbed is not None
assert adv.distance.value < np.inf
def test_attack_non_verbose(bn_adversarial):
adv = bn_adversarial
attack = BoundaryAttack()
attack(adv, iterations=200, verbose=False)
assert adv.perturbed is not None
assert adv.distance.value < np.inf