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Although there are a great number of adversarial attacks on deep learningbased classifiers, how to attack object detection systems has been rarelystudied. In this paper, we propose a Half-Neighbor Masked Projected GradientDescent (HNM-PGD) based attack, which can generate strong perturbation to fooldifferent kinds of detectors under strict constraints. We also applied theproposed HNM-PGD attack in the CIKM 2020 AnalytiCup Competition, which wasranked within the top 1% on the leaderboard. We release the code athttps://github.com/YanghaoZYH/HNM-PGD.

Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks