Adversarial Masked Autoencoders Are Robust Vision Learners

Abstract

Self-supervised learning, specifically masked image modeling, has achieved significant success, surpassing earlier contrastive learning methods. However, the robustness of these methods against adversarial attacks, which subtly manipulate inputs to mislead models, remains largely unexplored. This study investigates the adversarial robustness of self-supervised learning methods, exposing their vulnerabilities to various adversarial attacks. We introduce adversarial masked autoencoders (AMAEs), a novel framework designed to enforce adversarial robustness during the masked image modeling process. Through extensive experiments on four classification benchmarks involving eight different adversarial attacks, we demonstrate that AMAE consistently outperforms seven state-of-the-art baseline self-supervised learning methods in terms of adversarial robustness.