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[R][OpenAI] Testing Robustness Against Unforeseen Adversaries

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

submitted by /u/downtownslim
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.