GAP++: Learning to generate target-conditioned adversarial examples

Understand

Adversarial examples are perturbed inputs which can cause a serious threat for machine learning models.

• Finding these perturbations is such a hard task that we can only use the iterative methods to traverse.
• For computational efficiency, recent works use adversarial generative networks to model the distribution of both the universal or image-dependent perturbations directly.
• However, these methods generate perturbations only rely on input images.