GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification. (arXiv:1905.11475v4 [cs.LG] UPDATED)

The vulnerabilities of deep neural networks against adversarial examples have
become a significant concern for deploying these models in sensitive domains.
Devising a definitive defense against such attacks is proven to be challenging,
and the methods relying on detecting adversarial samples are only valid when
the attacker is oblivious to the detection mechanism. In this paper we propose
a principled adversarial example detection method that can withstand
norm-constrained white-box attacks. Inspired by one-versus-the-rest
classification, in a K class classification problem, …

adversarial classification detection training