eta=1.05,
beta=0.9,
eps=10.,
)),
] # each element in the list is the tuple (attack_class, attack_kwargs)
mnist_clntrained_model = get_mnist_lenet5_clntrained().to(device)
mnist_advtrained_model = get_mnist_lenet5_advtrained().to(device)
mnist_test_loader = get_mnist_test_loader(batch_size=batch_size)
lst_setting = [
(mnist_clntrained_model, mnist_test_loader),
(mnist_advtrained_model, mnist_test_loader),
]
info = get_benchmark_sys_info()
lst_benchmark = []
for model, loader in lst_setting:
for attack_class, attack_kwargs in lst_attack:
lst_benchmark.append(
benchmark_margin(model,
loader,
attack_class,
attack_kwargs,
norm=attack_kwargs["norm"]))
print(info)
for item in lst_benchmark:
print(item)
batch_size = 100
device = "cuda"
lst_attack = [
(DDNL2Attack, dict(
nb_iter=1000, gamma=0.05, init_norm=1., quantize=True, levels=256,
clip_min=0., clip_max=1., targeted=False)),
] # each element in the list is the tuple (attack_class, attack_kwargs)
mnist_clntrained_model = get_mnist_lenet5_clntrained().to(device)
mnist_advtrained_model = get_mnist_lenet5_advtrained().to(device)
mnist_test_loader = get_mnist_test_loader(batch_size=batch_size)
lst_setting = [
(mnist_clntrained_model, mnist_test_loader),
(mnist_advtrained_model, mnist_test_loader),
]
info = get_benchmark_sys_info()
lst_benchmark = []
for model, loader in lst_setting:
for attack_class, attack_kwargs in lst_attack:
lst_benchmark.append(benchmark_margin(
model, loader, attack_class, attack_kwargs, norm=2, device="cuda"))
print(info)
for item in lst_benchmark:
print(item)
(CarliniWagnerLinfAttack,
dict(num_classes=10, max_iterations=500, max_const=0.1,
return_best=True)),
] # each element in the list is the tuple (attack_class, attack_kwargs)
mnist_clntrained_model = get_mnist_lenet5_clntrained().to(device)
mnist_advtrained_model = get_mnist_lenet5_advtrained().to(device)
mnist_test_loader = get_mnist_test_loader(batch_size=batch_size)
lst_setting = [
(mnist_clntrained_model, mnist_test_loader),
(mnist_advtrained_model, mnist_test_loader),
]
info = get_benchmark_sys_info()
lst_benchmark = []
for model, loader in lst_setting:
for attack_class, attack_kwargs in lst_attack:
lst_benchmark.append(
benchmark_margin(model,
loader,
attack_class,
attack_kwargs,
"inf",
device="cuda"))
print(info)
for item in lst_benchmark:
print(item)
dict(nb_iter=1000,
gamma=0.05,
init_norm=1.,
quantize=True,
levels=256,
clip_min=0.,
clip_max=1.,
targeted=False)),
] # each element in the list is the tuple (attack_class, attack_kwargs)
mnist_clntrained_model = get_mnist_lenet5_clntrained().to(device)
mnist_advtrained_model = get_mnist_lenet5_advtrained().to(device)
mnist_test_loader = get_mnist_test_loader(batch_size=batch_size)
lst_setting = [
(mnist_clntrained_model, mnist_test_loader),
(mnist_advtrained_model, mnist_test_loader),
]
info = get_benchmark_sys_info()
lst_benchmark = []
for model, loader in lst_setting:
for attack_class, attack_kwargs in lst_attack:
lst_benchmark.append(
benchmark_margin(model, loader, attack_class, attack_kwargs, 2))
print(info)
for item in lst_benchmark:
print(item)