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arXiv · 2204.03714

Using Multiple Self-Supervised Tasks Improves Model Robustness

Matthew Lawhon·Chengzhi Mao·Junfeng Yang
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Abstract

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.

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Matthew Lawhon, Chengzhi Mao, Junfeng Yang. 2022-04-07. Using Multiple Self-Supervised Tasks Improves Model Robustness. https://arxiv.org/abs/2204.03714

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