We’ve developed a method to assess whether a neural network classifier can reliably defend against adversarial attacks not seen during training. Our method yields a new metric, UAR (Unforeseen Attack Robustness), which evaluates the robustness of a single model against an unanticipated attack, and highlights the need to measure performance across a more diverse range of unforeseen attacks.

Website: https://openai.com/blog/testing-robustness/

Paper: http://arxiv.org/abs/1908.08016

Code: https://github.com/ddkang/advex-uar