Yueru Li

Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds

Deep neural networks are vulnerable to adversarial attacks. Though various attempts have been made, it is still largely open to fully understand the existence of adversarial samples and thereby develop effective defense strategies. In this paper, we present a new perspective, namely gradient leaking…