558 citations · 3.6k across the 65 of their papers we have counts for
Showing 2019 · cs.LGShow all
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cs.LG2019★ 78 cited
Testing Robustness Against Unforeseen Adversaries
Max Kaufmann, Daniel Kang, Yi Sun +9
Adversarial robustness research primarily focuses on L_p perturbations, and most defenses are developed with identical training-time and test-time adversaries. However, in real-wor…
cs.LG2019★ 184 cited
Natural Adversarial Examples
Dan Hendrycks, Kevin Zhao, Steven Basart +2
We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtrati…
cs.LG2019★ 25 cited
Transfer of Adversarial Robustness Between Perturbation Types
Daniel Kang, Yi Sun, Tom Brown +2
We study the transfer of adversarial robustness of deep neural networks between different perturbation types. While most work on adversarial examples has focused on and…