Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks

Yanghao Zhang; Fu Wang; Wenjie Ruan

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arXiv · 2101.00989

Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks

Yanghao Zhang·Fu Wang·Wenjie Ruan

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Abstract

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.

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BibTeX RIS

Yanghao Zhang, Fu Wang, Wenjie Ruan. 2021-01-04. Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks. https://arxiv.org/abs/2101.00989

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