Lin Shi

Generalization of Defense Effects Learned from a Single Adversarial Attack

Adversarial attacks misled deep neural networks by injecting perturbations into input images. Training networks with adversarial examples defended against adversarial attacks. However, training with specific adversarial examples only defended against the corresponding attacks. To generalize the defense effect from one...