Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks

Fu Wang; Wenjie Ruan; Yanghao Zhang

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Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks

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arxiv 2101.00989 v1 pith:FJEKR2OE submitted 2021-01-04 cs.CV cs.LG

Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks

Yanghao Zhang , Fu Wang , Wenjie Ruan This is my paper

classification cs.CV cs.LG

keywords attackhnm-pgdadversarialattacksdetectorshalf-neighborobjectalthough

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Although there are a great number of adversarial attacks on deep learning based classifiers, how to attack object detection systems has been rarely studied. In this paper, we propose a Half-Neighbor Masked Projected Gradient Descent (HNM-PGD) based attack, which can generate strong perturbation to fool different kinds of detectors under strict constraints. We also applied the proposed HNM-PGD attack in the CIKM 2020 AnalytiCup Competition, which was ranked within the top 1% on the leaderboard. We release the code at https://github.com/YanghaoZYH/HNM-PGD.

Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks

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