def test_attack_random_start(self) -> None:
model = BasicModel()
input = torch.tensor([[2.0, -9.0, 9.0, 1.0, -3.0]])
adv = PGD(model)
perturbed_input = adv.perturb(input,
0.25,
0.1,
0,
4,
random_start=True)
assertTensorAlmostEqual(
self,
perturbed_input,
[[2.0, -9.0, 9.0, 1.0, -3.0]],
delta=0.25,
mode="max",
)
perturbed_input = adv.perturb(input,
0.25,
0.1,
0,
4,
norm="L2",
random_start=True)
norm = torch.norm((perturbed_input - input).squeeze()).numpy()
self.assertLessEqual(norm, 0.25)
def test_attack_targeted(self) -> None:
model = BasicModel()
input = torch.tensor([[9.0, 10.0, -6.0, -1.0]], requires_grad=True)
adv = PGD(model)
perturbed_input = adv.perturb(input, 0.2, 0.1, 3, 3, targeted=True)
assertArraysAlmostEqual(torch.flatten(perturbed_input).tolist(),
[9.0, 10.0, -6.0, -1.2],
delta=0.01)
def test_attack_nontargeted(self) -> None:
model = BasicModel()
input = torch.tensor([[2.0, -9.0, 9.0, 1.0, -3.0]])
adv = PGD(model)
perturbed_input = adv.perturb(input, 0.25, 0.1, 2, 4)
assertArraysAlmostEqual(
torch.flatten(perturbed_input).tolist(),
[2.0, -9.0, 9.0, 1.0, -2.8],
delta=0.01,
)
def test_attack_3dimensional_input(self) -> None:
model = BasicModel()
input = torch.tensor(
[[[4.0, 2.0], [-1.0, -2.0]], [[3.0, -4.0], [10.0, 5.0]]],
requires_grad=True)
adv = PGD(model)
perturbed_input = adv.perturb(input, 0.25, 0.1, 3, (0, 1))
assertArraysAlmostEqual(
torch.flatten(perturbed_input).tolist(),
[4.0, 2.0, -1.0, -2.0, 3.0, -3.75, 10.0, 5.0],
delta=0.01,
)
def test_attack_nontargeted(self) -> None:
model = BasicModel()
input = torch.tensor([[2.0, -9.0, 9.0, 1.0, -3.0]])
adv = PGD(model)
perturbed_input = adv.perturb(input, 0.25, 0.1, 2, 4)
assertTensorAlmostEqual(
self,
perturbed_input,
[[2.0, -9.0, 9.0, 1.0, -2.8]],
delta=0.01,
mode="max",
)
def test_attack_targeted(self) -> None:
model = BasicModel()
input = torch.tensor([[9.0, 10.0, -6.0, -1.0]], requires_grad=True)
adv = PGD(model)
perturbed_input = adv.perturb(input, 0.2, 0.1, 3, 3, targeted=True)
assertTensorAlmostEqual(
self,
perturbed_input,
[[9.0, 10.0, -6.0, -1.2]],
delta=0.01,
mode="max",
)
def test_attack_3dimensional_input(self) -> None:
model = BasicModel()
input = torch.tensor(
[[[4.0, 2.0], [-1.0, -2.0]], [[3.0, -4.0], [10.0, 5.0]]],
requires_grad=True)
adv = PGD(model)
perturbed_input = adv.perturb(input, 0.25, 0.1, 3, (0, 1))
assertTensorAlmostEqual(
self,
perturbed_input,
[[[4.0, 2.0], [-1.0, -2.0]], [[3.0, -3.75], [10.0, 5.0]]],
delta=0.01,
mode="max",
)
def test_attack_loss_defined(self) -> None:
model = BasicModel_MultiLayer()
add_input = torch.tensor([[-1.0, 2.0, 2.0]])
input = torch.tensor([[1.0, 6.0, -3.0]])
labels = torch.tensor([0])
loss_func = CrossEntropyLoss(reduction="none")
adv = PGD(model, loss_func)
perturbed_input = adv.perturb(input,
0.25,
0.1,
3,
labels,
additional_forward_args=(add_input, ))
assertArraysAlmostEqual(perturbed_input.squeeze(0).tolist(),
[1.0, 6.0, -3.0],
delta=0.01)
def test_attack_multiinput(self) -> None:
model = BasicModel2()
input1 = torch.tensor([[4.0, -1.0], [3.0, 10.0]], requires_grad=True)
input2 = torch.tensor([[2.0, -5.0], [-2.0, 1.0]], requires_grad=True)
adv = PGD(model)
perturbed_input = adv.perturb((input1, input2),
0.25,
0.1,
3,
0,
norm="L2")
answer = ([3.75, -1.0, 2.75, 10.0], [2.25, -5.0, -2.0, 1.0])
for i in range(len(perturbed_input)):
assertArraysAlmostEqual(torch.flatten(perturbed_input[i]).tolist(),
answer[i],
delta=0.01)