Learning Defense Transformers for Counterattacking Adversarial Examples

TL;DR

This paper introduces a novel defense transformer that uses affine transformations to counterattack and restore adversarial examples, enhancing the robustness of deep neural networks against various attack types.

Contribution

It proposes a new approach to defend against unknown adversarial attacks by learning a transformation that pulls adversarial examples back to the original data distribution.

Findings

The defense transformer effectively restores adversarial examples in experiments.

It generalizes well across different datasets and attack types.

The method improves robustness of DNNs against adversarial attacks.

Abstract

Deep neural networks (DNNs) are vulnerable to adversarial examples with small perturbations. Adversarial defense thus has been an important means which improves the robustness of DNNs by defending against adversarial examples. Existing defense methods focus on some specific types of adversarial examples and may fail to defend well in real-world applications. In practice, we may face many types of attacks where the exact type of adversarial examples in real-world applications can be even unknown. In this paper, motivated by that adversarial examples are more likely to appear near the classification boundary, we study adversarial examples from a new perspective that whether we can defend against adversarial examples by pulling them back to the original clean distribution. We theoretically and empirically verify the existence of defense affine transformations that restore adversarial examples. Relying on this, we learn a defense transformer to counterattack the adversarial examples by parameterizing the affine transformations and exploiting the boundary information of DNNs. Extensive experiments on both toy and real-world datasets demonstrate the effectiveness and generalization of our defense transformer.