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
def get_attack(attack, fmodel):
args = []
kwargs = {}
# L0
if attack == 'SAPA':
metric = foolbox.distances.L0
A = fa.SaltAndPepperNoiseAttack(fmodel, distance = metric)
elif attack == 'PA':
metric = foolbox.distances.L0
A = fa.PointwiseAttack(fmodel, distance = metric)
# L2
elif 'IGD' in attack:
metric = foolbox.distances.MSE
A = fa.L2BasicIterativeAttack(fmodel, distance = metric)
# kwargs['epsilons'] = 1.5
elif attack == 'AGNA':
metric = foolbox.distances.MSE
kwargs['epsilons'] = np.linspace(0.5, 1, 50)
A = fa.AdditiveGaussianNoiseAttack(fmodel, distance = metric)
elif attack == 'BA':
metric = foolbox.distances.MSE
A = fa.BoundaryAttack(fmodel, distance = metric)
kwargs['log_every_n_steps'] = 500001
elif 'DeepFool' in attack:
metric = foolbox.distances.MSE
A = fa.DeepFoolL2Attack(fmodel, distance = metric)
elif attack == 'PAL2':
metric = foolbox.distances.MSE
A = fa.PointwiseAttack(fmodel, distance = metric)
elif attack == "CWL2":
metric = foolbox.distances.MSE
A = fa.CarliniWagnerL2Attack(fmodel, distance = metric)
# L inf
elif 'FGSM' in attack and not 'IFGSM' in attack:
metric = foolbox.distances.Linf
A = fa.FGSM(fmodel, distance = metric)
kwargs['epsilons'] = 20
elif 'PGD' in attack:
metric = foolbox.distances.Linf
A = fa.LinfinityBasicIterativeAttack(fmodel, distance = metric)
elif 'IGM' in attack:
metric = foolbox.distances.Linf
A = fa.MomentumIterativeAttack(fmodel, distance = metric)
else:
raise Exception('Not implemented')
return A, metric, args, kwargs
steps=20,
decision_rule="L1",
regularization=0),
uses_grad=True,
),
AttackTestTarget(fa.NewtonFoolAttack(steps=20), uses_grad=True),
AttackTestTarget(fa.VirtualAdversarialAttack(steps=50, xi=1),
10,
uses_grad=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),
AttackTestTarget(fa.FGSM(), Linf(100.0), uses_grad=True),
AttackTestTarget(FGSM_GE(), Linf(100.0)),
AttackTestTarget(fa.FGM(), L2(100.0), uses_grad=True),
AttackTestTarget(fa.L1FastGradientAttack(), 5000.0, uses_grad=True),
AttackTestTarget(fa.GaussianBlurAttack(steps=10),
uses_grad=True,
requires_real_model=True),
AttackTestTarget(
fa.GaussianBlurAttack(steps=10, max_sigma=224.0),
uses_grad=True,
requires_real_model=True,
),
AttackTestTarget(fa.L2DeepFoolAttack(steps=50, loss="logits"),
),
(
fa.EADAttack(binary_search_steps=3,
steps=20,
decision_rule="L1",
regularization=0),
None,
True,
False,
),
(fa.NewtonFoolAttack(steps=20), None, True, False),
(fa.VirtualAdversarialAttack(steps=50, xi=1), 10, True, False),
(fa.PGD(), Linf(1.0), True, False),
(fa.L2PGD(), L2(50.0), True, False),
(fa.LinfBasicIterativeAttack(abs_stepsize=0.2), Linf(1.0), True, False),
(fa.L2BasicIterativeAttack(), L2(50.0), True, False),
(fa.FGSM(), Linf(100.0), True, False),
(fa.FGM(), L2(100.0), True, False),
(fa.GaussianBlurAttack(steps=10), None, True, True),
(fa.GaussianBlurAttack(steps=10, max_sigma=224.0), None, True, True),
(fa.L2DeepFoolAttack(steps=50, loss="logits"), None, True, False),
(fa.L2DeepFoolAttack(steps=50, loss="crossentropy"), None, True, False),
(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,