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Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning. While a lot of efforts have been made in recent years, it is of great significance to perform correct and complete evaluations of the adversarial attack and defense algorithms. In this paper, we establish a comprehensive, rigorous, and coherent benchmark to evaluate adversarial robustness on image classification tasks. After brieflydoi:10.1109/cvpr42600.2020.00040 dblp:conf/cvpr/DongFYPSXZ20 fatcat:bglxvcjgy5hlfmecqgylz2tabi