GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification

TL;DR

This paper introduces a novel adversarial detection method called GAT, which uses one-vs-rest binary classifiers and generative modeling to detect adversarial examples effectively, even against white-box attacks.

Contribution

The paper proposes GAT, a principled adversarial detection framework combining one-vs-rest classifiers and generative modeling, providing robust detection against white-box adversarial attacks.

Findings

Competitive detection performance demonstrated

Effective against norm-constrained white-box attacks

Generative approach enhances detection robustness

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 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, we train K binary classifiers where the i-th binary classifier is used to distinguish between clean data of class i and adversarially perturbed samples of other classes. At test time, we first use a trained classifier to get the predicted label (say k) of the input, and then use the k-th binary classifier to determine whether the input is a clean sample (of class k) or an adversarially perturbed example (of other classes). We further devise a generative approach to detecting/classifying adversarial examples by interpreting each binary classifier as an unnormalized density model of the class-conditional data. We provide comprehensive evaluation of the above adversarial example detection/classification methods, and demonstrate their competitive performances and compelling properties.