  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.
