GAT: Generative Adversarial Training for Adversarial Example Detection and Robust ClassificationDownload PDF

25 Sep 2019 (modified: 09 May 2020)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • TL;DR: We propose an objective that could be used for training adversarial example detection and robust classification systems.
  • Abstract: 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 present an adversarial example detection method that provides performance guarantee to norm constrained adversaries. The method is based on the idea of training adversarial robust subspace detectors using generative adversarial training (GAT). The novel GAT objective presents a saddle point problem similar to that of GANs; it has the same convergence property, and consequently supports the learning of class conditional distributions. We demonstrate that the saddle point problem could be reasonably solved by PGD attack, and further use the learned class conditional generative models to define generative detection/classification models that are both robust and more interpretable. We provide comprehensive evaluations of the above methods, and demonstrate their competitive performances and compelling properties on adversarial detection and robust classification problems.
  • Keywords: adversarial example detection, adversarial examples classification, robust optimization, ML security, generative modeling, generative classification
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