Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach

TL;DR

This paper introduces a non-iterative, teacher-guided curriculum learning approach that enhances adversarial robustness by aligning attribution maps and restricting perturbations to object pixels, achieving superior accuracy with less training effort.

Contribution

It proposes a novel non-iterative training method that improves adversarial robustness by focusing perturbations on object pixels and aligning attribution maps, outperforming existing adversarial training techniques.

Findings

Significant performance improvements over existing adversarial training methods.

Attribution maps in robust models are more aligned with actual objects.

Method reduces training time by 10-20% while enhancing robustness.

Abstract

Current SOTA adversarially robust models are mostly based on adversarial training (AT) and differ only by some regularizers either at inner maximization or outer minimization steps. Being repetitive in nature during the inner maximization step, they take a huge time to train. We propose a non-iterative method that enforces the following ideas during training. Attribution maps are more aligned to the actual object in the image for adversarially robust models compared to naturally trained models. Also, the allowed set of pixels to perturb an image (that changes model decision) should be restricted to the object pixels only, which reduces the attack strength by limiting the attack space. Our method achieves significant performance gains with a little extra effort (10-20%) over existing AT models and outperforms all other methods in terms of adversarial as well as natural accuracy. We have performed extensive experimentation with CIFAR-10, CIFAR-100, and TinyImageNet datasets and reported results against many popular strong adversarial attacks to prove the effectiveness of our method.