epsilon: Optional[float] = None
uses_grad: Optional[bool] = False
requires_real_model: Optional[bool] = False
def get_attack_id(x: AttackTestTarget) -> str:
return repr(x.attack)
# attack, eps / None, attack_uses_grad, requires_real_model
attacks: List[AttackTestTarget] = [
AttackTestTarget(fa.DDNAttack(init_epsilon=2.0), uses_grad=True),
AttackTestTarget(fa.InversionAttack()),
AttackTestTarget(
fa.InversionAttack(distance=fbn.distances.l2).repeat(3).repeat(2), ),
AttackTestTarget(fa.L2ContrastReductionAttack(), L2(100.0)),
AttackTestTarget(fa.L2ContrastReductionAttack().repeat(3), 100.0),
AttackTestTarget(
fa.BinarySearchContrastReductionAttack(binary_search_steps=15)),
AttackTestTarget(fa.LinearSearchContrastReductionAttack(steps=20)),
AttackTestTarget(fa.L2CarliniWagnerAttack(binary_search_steps=11, steps=5),
uses_grad=True),
AttackTestTarget(
fa.L2CarliniWagnerAttack(binary_search_steps=3,
steps=20,
confidence=2.0),
uses_grad=True,
),
AttackTestTarget(fa.EADAttack(binary_search_steps=10,
steps=20,
regularization=0),
def get_attack_id(x: Tuple[fbn.Attack, bool, bool]) -> str:
return repr(x[0])
# attack, eps / None, attack_uses_grad, requires_real_model
attacks: List[Tuple[fbn.Attack, Optional[float], bool, bool]] = [
(fa.DDNAttack(init_epsilon=2.0), None, True, False),
(fa.InversionAttack(), None, False, False),
(
fa.InversionAttack(distance=fbn.distances.l2).repeat(3).repeat(2),
None,
False,
False,
),
(fa.L2ContrastReductionAttack(), L2(100.0), False, False),
(fa.L2ContrastReductionAttack().repeat(3), 100.0, False, False),
(
fa.BinarySearchContrastReductionAttack(binary_search_steps=15),
None,
False,
False,
),
(fa.LinearSearchContrastReductionAttack(steps=20), None, False, False),
(fa.L2CarliniWagnerAttack(binary_search_steps=11,
steps=5), None, True, False),
(
fa.L2CarliniWagnerAttack(binary_search_steps=3,
steps=20,
confidence=2.0),
None,