An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness

Understand

Deep neural networks (DNNs) have had many successes, but they suffer from two major issues: (1) a vulnerability to adversarial examples and (2) a tendency to elude human interpretation.

• Interestingly, recent empirical and theoretical evidence suggests these two seemingly disparate issues are actually connected.
• In particular, robust models tend to provide more interpretable gradients than non-robust models.
• However, whether this relationship works in the opposite direction remains obscure.