Proper Network Interpretability Helps Adversarial Robustness in Classification

TL;DR

This paper demonstrates that proper interpretability measurement reveals the difficulty of preventing adversarial attacks from causing interpretation discrepancies, and proposes a defense that enhances both robustness of classification and interpretation.

Contribution

The paper provides a theoretical analysis linking interpretability measurement to adversarial vulnerability and introduces a novel interpretability-aware defense method.

Findings

Interpretability measurement impacts adversarial robustness.

The proposed defense improves robustness of both classification and interpretation.

Outperforms existing adversarial training methods against large perturbation attacks.

Abstract

Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visually similar), or interpretability is itself susceptible to adversarial attacks. In this paper, we theoretically show that with a proper measurement of interpretation, it is actually difficult to prevent prediction-evasion adversarial attacks from causing interpretation discrepancy, as confirmed by experiments on MNIST, CIFAR-10 and Restricted ImageNet. Spurred by that, we develop an interpretability-aware defensive scheme built only on promoting robust interpretation (without the need for resorting to adversarial loss minimization). We show that our defense achieves both robust classification and robust interpretation, outperforming state-of-the-art adversarial training methods against attacks of large perturbation in particular.