False,
False,
),
(fa.SaltAndPepperNoiseAttack(steps=50), None, True, False),
(fa.SaltAndPepperNoiseAttack(steps=50, channel_axis=1), None, True, False),
(fa.LinearSearchBlendedUniformNoiseAttack(steps=50), None, False, False),
(fa.L2AdditiveGaussianNoiseAttack(), 2500.0, False, False),
(fa.LinfAdditiveUniformNoiseAttack(), 10.0, False, False),
(
fa.L2RepeatedAdditiveGaussianNoiseAttack(check_trivial=False),
1000.0,
False,
False,
),
(fa.L2RepeatedAdditiveGaussianNoiseAttack(), 1000.0, False, False),
(fa.L2RepeatedAdditiveUniformNoiseAttack(), 1000.0, False, False),
(fa.LinfRepeatedAdditiveUniformNoiseAttack(), 3.0, False, False),
]
@pytest.mark.parametrize("attack_eps_grad_real", attacks, ids=get_attack_id)
def test_untargeted_attacks(
fmodel_and_data_ext_for_attacks: Tuple[Tuple[fbn.Model, ep.Tensor,
ep.Tensor], bool],
attack_eps_grad_real: Tuple[fbn.Attack, Optional[float], bool, bool],
) -> None:
attack, eps, attack_uses_grad, requires_real_model = attack_eps_grad_real
(fmodel, x, y), real = fmodel_and_data_ext_for_attacks
if requires_real_model and not real:
pytest.skip()
uses_grad=True),
AttackTestTarget(fa.LinearSearchBlendedUniformNoiseAttack(steps=50), None),
AttackTestTarget(fa.L2AdditiveGaussianNoiseAttack(), 2500.0),
AttackTestTarget(fa.L2ClippingAwareAdditiveGaussianNoiseAttack(), 500.0),
AttackTestTarget(fa.LinfAdditiveUniformNoiseAttack(), 10.0),
AttackTestTarget(
fa.L2RepeatedAdditiveGaussianNoiseAttack(check_trivial=False), 1000.0),
AttackTestTarget(
fa.L2ClippingAwareRepeatedAdditiveGaussianNoiseAttack(
check_trivial=False),
200.0,
),
AttackTestTarget(fa.L2RepeatedAdditiveGaussianNoiseAttack(), 1000.0),
AttackTestTarget(fa.L2ClippingAwareRepeatedAdditiveGaussianNoiseAttack(),
200.0),
AttackTestTarget(fa.L2RepeatedAdditiveUniformNoiseAttack(), 1000.0),
AttackTestTarget(fa.L2ClippingAwareRepeatedAdditiveUniformNoiseAttack(),
200.0),
AttackTestTarget(fa.LinfRepeatedAdditiveUniformNoiseAttack(), 3.0),
]
@pytest.mark.parametrize("attack_test_target", attacks, ids=get_attack_id)
def test_untargeted_attacks(
fmodel_and_data_ext_for_attacks: ModelDescriptionAndData,
attack_test_target: AttackTestTarget,
) -> None:
(fmodel, x, y), real = fmodel_and_data_ext_for_attacks
if attack_test_target.requires_real_model and not real:
pytest.skip()
),
AttackTestTarget(
fa.L2ClippingAwareRepeatedAdditiveGaussianNoiseAttack(check_trivial=False),
200.0,
stochastic_attack=True,
),
AttackTestTarget(
fa.L2RepeatedAdditiveGaussianNoiseAttack(), 1000.0, stochastic_attack=True
),
AttackTestTarget(
fa.L2ClippingAwareRepeatedAdditiveGaussianNoiseAttack(),
200.0,
stochastic_attack=True,
),
AttackTestTarget(
fa.L2RepeatedAdditiveUniformNoiseAttack(), 1000.0, stochastic_attack=True
),
AttackTestTarget(
fa.L2ClippingAwareRepeatedAdditiveUniformNoiseAttack(),
200.0,
stochastic_attack=True,
),
AttackTestTarget(
fa.LinfRepeatedAdditiveUniformNoiseAttack(), 3.0, stochastic_attack=True
),
]
@pytest.mark.parametrize("attack_test_target", attacks, ids=get_attack_id)
def test_untargeted_attacks(
fmodel_and_data_ext_for_attacks: ModeAndDataAndDescription,