On Saliency Maps and Adversarial Robustness

TL;DR

This paper introduces Saliency-based Adversarial Training (SAT), a novel method that leverages existing dataset annotations as weak saliency maps to enhance the adversarial robustness of models across multiple datasets.

Contribution

The paper proposes SAT, a new approach that uses weak saliency maps from dataset annotations to improve adversarial robustness without extra perturbation generation, and demonstrates its effectiveness empirically.

Findings

SAT improves adversarial robustness on multiple datasets.

Finer saliency maps lead to more robust models.

Combining SAT with existing methods further boosts performance.

Abstract

A Very recent trend has emerged to couple the notion of interpretability and adversarial robustness, unlike earlier efforts which solely focused on good interpretations or robustness against adversaries. Works have shown that adversarially trained models exhibit more interpretable saliency maps than their non-robust counterparts, and that this behavior can be quantified by considering the alignment between input image and saliency map. In this work, we provide a different perspective to this coupling, and provide a method, Saliency based Adversarial training (SAT), to use saliency maps to improve adversarial robustness of a model. In particular, we show that using annotations such as bounding boxes and segmentation masks, already provided with a dataset, as weak saliency maps, suffices to improve adversarial robustness with no additional effort to generate the perturbations themselves. Our empirical results on CIFAR-10, CIFAR-100, Tiny ImageNet and Flower-17 datasets consistently corroborate our claim, by showing improved adversarial robustness using our method. saliency maps. We also show how using finer and stronger saliency maps leads to more robust models, and how integrating SAT with existing adversarial training methods, further boosts performance of these existing methods.