def attack_switcher(att, fmodel):
""" Initialize different attacks. """
switcher = {
"fgsm": fa.GradientSignAttack(fmodel, distance=Linf),
"bim": fa.LinfinityBasicIterativeAttack(fmodel, distance=Linf),
"mim": fa.MomentumIterativeAttack(fmodel, distance=Linf),
"df": LimitedDeepFoolL2Attack(fmodel),
"cw": LimitedCarliniWagnerL2Attack(fmodel),
"hsj": LimitedHopSkipJumpAttack(fmodel, distance=Linf),
"ga": fa.GenAttack(fmodel, criterion=TargetClass(9), distance=Linf),
}
return switcher.get(att)
uses_grad=True,
),
AttackTestTarget(fa.DDNAttack(init_epsilon=2.0, steps=20), uses_grad=True),
# TODO: targeted EADAttack currently fails repeatedly on MobileNetv2
AttackTestTarget(
fa.EADAttack(
binary_search_steps=3,
steps=20,
abort_early=True,
regularization=0,
initial_const=1e1,
),
uses_grad=True,
),
AttackTestTarget(
fa.GenAttack(steps=100, population=6, reduced_dims=(7, 7)),
epsilon=0.5,
requires_real_model=True,
),
AttackTestTarget(fa.PGD(), Linf(1.0), uses_grad=True),
AttackTestTarget(fa.L2PGD(), L2(50.0), uses_grad=True),
AttackTestTarget(fa.L1PGD(), 5000.0, uses_grad=True),
AttackTestTarget(fa.LinfBasicIterativeAttack(abs_stepsize=0.2),
Linf(1.0),
uses_grad=True),
AttackTestTarget(fa.L2BasicIterativeAttack(), L2(50.0), uses_grad=True),
AttackTestTarget(fa.L1BasicIterativeAttack(), 5000.0, uses_grad=True),
AttackTestTarget(fa.SparseL1DescentAttack(), 5000.0, uses_grad=True),
]
),
(fa.DDNAttack(init_epsilon=2.0, steps=20), None, True, False),
# TODO: targeted EADAttack currently fails repeatedly on MobileNetv2
(
fa.EADAttack(
binary_search_steps=3,
steps=20,
abort_early=True,
regularization=0,
initial_const=1e1,
),
None,
True,
False,
),
(fa.GenAttack(steps=100, population=6, reduced_dims=(14, 14)), 0.3, False, True),
]
@pytest.mark.parametrize("attack_eps_grad_real", targeted_attacks, ids=get_attack_id)
def test_targeted_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()
),
(fa.DDNAttack(init_epsilon=2.0, steps=20), None, True, False),
# TODO: targeted EADAttack currently fails repeatedly on MobileNetv2
(
fa.EADAttack(
binary_search_steps=3,
steps=20,
abort_early=True,
regularization=0,
initial_const=1e1,
),
None,
True,
False,
),
(fa.GenAttack(steps=100, population=6, reduced_dims=(7, 7)), 0.5, False, True),
]
@pytest.mark.parametrize("attack_eps_grad_real", targeted_attacks, ids=get_attack_id)
def test_targeted_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()