def __call__(self,
inputs,
labels,
*,
epsilon,
criterion,
repeats=100,
check_trivial=True):
originals = ep.astensor(inputs)
labels = ep.astensor(labels)
def is_adversarial(p: ep.Tensor) -> ep.Tensor:
"""For each input in x, returns true if it is an adversarial for
the given model and criterion"""
logits = self.model.forward(p)
return criterion(originals, labels, p, logits)
x0 = ep.astensor(inputs)
min_, max_ = self.model.bounds()
result = x0
if check_trivial:
found = is_adversarial(result)
else:
found = ep.zeros(x0, len(result)).bool()
for _ in range(repeats):
if found.all():
break
p = self.sample_noise(x0)
norms = self.get_norms(p)
p = p / atleast_kd(norms, p.ndim)
x = x0 + epsilon * p
x = x.clip(min_, max_)
is_adv = is_adversarial(x)
is_new_adv = ep.logical_and(is_adv, ep.logical_not(found))
result = ep.where(atleast_kd(is_new_adv, x.ndim), x, result)
found = ep.logical_or(found, is_adv)
return result.tensor
def run(
self,
model: Model,
inputs: T,
criterion: Union[Criterion, Any] = None,
*,
epsilon: float,
**kwargs: Any,
) -> T:
raise_if_kwargs(kwargs)
x0, restore_type = ep.astensor_(inputs)
criterion_ = get_criterion(criterion)
del inputs, criterion, kwargs
verify_input_bounds(x0, model)
is_adversarial = get_is_adversarial(criterion_, model)
min_, max_ = model.bounds
result = x0
if self.check_trivial:
found = is_adversarial(result)
else:
found = ep.zeros(x0, len(result)).bool()
for _ in range(self.repeats):
if found.all():
break
p = self.sample_noise(x0)
epsilons = self.get_epsilons(x0, p, epsilon, min_=min_, max_=max_)
x = x0 + epsilons * p
x = x.clip(min_, max_)
is_adv = is_adversarial(x)
is_new_adv = ep.logical_and(is_adv, ep.logical_not(found))
result = ep.where(atleast_kd(is_new_adv, x.ndim), x, result)
found = ep.logical_or(found, is_adv)
return restore_type(result)
def test_logical_not_manual(t: Tensor) -> None:
assert (ep.logical_not(t > 3) == (t <= 3)).all()
def test_logical_not(t: Tensor) -> Tensor:
return ep.logical_not(t > 3)
