Deep networks achieve state-of-the-art performance on computer vision tasks, yet they fail under adversarial attacks that are imperceptible to humans. In this paper, we propose a novel defense that can dynamically adapt the input using the intrinsic structure from multiple self-supervised tasks. By simultaneously using many self-supervised tasks, our defense avoids over-fitting the adapted image to one specific self-supervised task and restores more intrinsic structure in the image compared to a single self-supervised task approach. Our approach further improves robustness and clean accuracy significantly compared to the state-of-the-art single task self-supervised defense. Our work is the first to connect multiple self-supervised tasks to robustness, and suggests that we can achieve better robustness with more intrinsic signal from visual data.
@article{arxiv.2204.03714,
title = {Using Multiple Self-Supervised Tasks Improves Model Robustness},
author = {Matthew Lawhon and Chengzhi Mao and Junfeng Yang},
journal= {arXiv preprint arXiv:2204.03714},
year = {2022}
}
Comments
Accepted to ICLR 2022 Workshop on PAIR^2Struct: Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data