def __call__(
self,
inputs: ep.Tensor,
labels: ep.Tensor,
perturbed: ep.Tensor,
logits: ep.Tensor,
) -> ep.Tensor:
classes = logits.argmax(axis=-1)
return classes == self.target_classes
def targeted_is_adv(logits: ep.Tensor, target_classes: ep.Tensor,
confidence) -> ep.Tensor:
logits = logits - ep.onehot_like(logits, target_classes, value=confidence)
classes = logits.argmax(axis=-1)
return classes == target_classes
def untargeted_is_adv(logits: ep.Tensor, labels: ep.Tensor,
confidence) -> ep.Tensor:
logits = logits + ep.onehot_like(logits, labels, value=confidence)
classes = logits.argmax(axis=-1)
return classes != labels
def misclassification(
inputs: ep.Tensor, labels: ep.Tensor, perturbed: ep.Tensor, logits: ep.Tensor
) -> ep.Tensor:
classes = logits.argmax(axis=-1)
return classes != labels
