Scaling Adversarial Training to Large Perturbation Bounds

TL;DR

This paper introduces Oracle-Aligned Adversarial Training (OA-AT), a novel method that enhances neural network robustness against perceptible adversarial perturbations by aligning model predictions with an Oracle, achieving state-of-the-art results at large perturbation bounds.

Contribution

The paper proposes OA-AT, a new adversarial training approach that aligns model predictions with an Oracle to improve robustness against large, perceptible perturbations, surpassing existing methods.

Findings

Achieves state-of-the-art robustness at large epsilon bounds (16/255) on CIFAR-10.

Outperforms existing defenses like AWP, TRADES, PGD-AT at standard bounds (8/255).

Effectively handles perturbations that may flip Oracle predictions without changing true labels.

Abstract

The vulnerability of Deep Neural Networks to Adversarial Attacks has fuelled research towards building robust models. While most Adversarial Training algorithms aim at defending attacks constrained within low magnitude Lp norm bounds, real-world adversaries are not limited by such constraints. In this work, we aim to achieve adversarial robustness within larger bounds, against perturbations that may be perceptible, but do not change human (or Oracle) prediction. The presence of images that flip Oracle predictions and those that do not makes this a challenging setting for adversarial robustness. We discuss the ideal goals of an adversarial defense algorithm beyond perceptual limits, and further highlight the shortcomings of naively extending existing training algorithms to higher perturbation bounds. In order to overcome these shortcomings, we propose a novel defense, Oracle-Aligned Adversarial Training (OA-AT), to align the predictions of the network with that of an Oracle during adversarial training. The proposed approach achieves state-of-the-art performance at large epsilon bounds (such as an L-inf bound of 16/255 on CIFAR-10) while outperforming existing defenses (AWP, TRADES, PGD-AT) at standard bounds (8/255) as well.