Paper: https://arxiv.org/abs/1905.01019

We analyze adversarial examples using techniques from high-dimensional geometry. We demonstrate several problems with the standard formulation of adversarial training using Lp-balls centered on the data, which occur in high-dimensional space. From these insights, we propose a new geometric constraint to replace the Lp-balls which gives improved robustness in some settings.

I’m the lead author of this article and would be happy to answer any questions!