def get_attack(attack, fmodel):
args = []
kwargs = {}
# L0
if attack == 'SAPA':
A = fa.SaltAndPepperNoiseAttack()
elif attack == 'PA':
A = fa.L1BrendelBethgeAttack()
# L2
elif 'IGD' in attack:
A = fa.L2BasicIterativeAttack()
elif attack == 'AGNA':
A = fa.L2AdditiveGaussianNoiseAttack()
elif attack == 'BA':
A = fa.BoundaryAttack()
elif 'DeepFool' in attack:
A = fa.L2DeepFoolAttack()
elif attack == 'PAL2':
A = fa.L2BrendelBethgeAttack()
elif attack == "CWL2":
A = fa.L2CarliniWagnerAttack()
# L inf
elif 'FGSM' in attack and not 'IFGSM' in attack:
A = fa.FGSM()
elif 'PGD' in attack:
A = fa.LinfPGD()
elif 'IGM' in attack:
A = fa.LinfBrendelBethgeAttack()
else:
raise Exception('Not implemented')
return A, 0, 0, 0
AttackTestTarget(fa.LinfDeepFoolAttack(steps=50), uses_grad=True),
AttackTestTarget(fa.BoundaryAttack(steps=50)),
AttackTestTarget(
fa.BoundaryAttack(
steps=110,
init_attack=fa.LinearSearchBlendedUniformNoiseAttack(steps=50),
update_stats_every_k=1,
)),
AttackTestTarget(fa.SaltAndPepperNoiseAttack(steps=50),
None,
uses_grad=True),
AttackTestTarget(fa.SaltAndPepperNoiseAttack(steps=50, channel_axis=1),
None,
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),
(fa.LinfDeepFoolAttack(steps=50), None, True, False),
(fa.BoundaryAttack(steps=50), None, False, False),
(
fa.BoundaryAttack(
steps=110,
init_attack=fa.LinearSearchBlendedUniformNoiseAttack(steps=50),
update_stats_every_k=1,
),
None,
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(
fa.SaltAndPepperNoiseAttack(steps=50),
None,
uses_grad=True,
stochastic_attack=True,
),
AttackTestTarget(
fa.SaltAndPepperNoiseAttack(steps=50, channel_axis=1),
None,
uses_grad=True,
stochastic_attack=True,
),
AttackTestTarget(
fa.LinearSearchBlendedUniformNoiseAttack(steps=50), None, stochastic_attack=True
),
AttackTestTarget(
fa.L2AdditiveGaussianNoiseAttack(), 3000.0, stochastic_attack=True
),
AttackTestTarget(
fa.L2ClippingAwareAdditiveGaussianNoiseAttack(), 500.0, stochastic_attack=True
),
AttackTestTarget(fa.LinfAdditiveUniformNoiseAttack(), 10.0, stochastic_attack=True),
AttackTestTarget(
fa.L2RepeatedAdditiveGaussianNoiseAttack(check_trivial=False),
1000.0,
stochastic_attack=True,
),
AttackTestTarget(
fa.L2ClippingAwareRepeatedAdditiveGaussianNoiseAttack(check_trivial=False),
200.0,
stochastic_attack=True,
),