![]() ![]() In this paper, we combine canonical supervised learning with self-supervised representation learning, and present Self-supervised Online Adversar-ial Purification (SOAP), a novel defense strategy that uses a self-supervised loss to purify adversarial examples at test-time. Deep neural networks are known to be vulnerable to adversarial examples, where a perturbation in the input space leads to an amplified shift in the latent network representation. ![]()
0 Comments
Leave a Reply. |