Using Multiple Self-Supervised Tasks Improves Model Robustness

Understand

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.