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HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design

Authors :
Wei, Hui
Wang, Zhixiang
Jia, Xuemei
Zheng, Yinqiang
Tang, Hao
Satoh, Shin'ichi
Wang, Zheng
Publication Year :
2022

Abstract

Adversarial attacks on thermal infrared imaging expose the risk of related applications. Estimating the security of these systems is essential for safely deploying them in the real world. In many cases, realizing the attacks in the physical space requires elaborate special perturbations. These solutions are often \emph{impractical} and \emph{attention-grabbing}. To address the need for a physically practical and stealthy adversarial attack, we introduce \textsc{HotCold} Block, a novel physical attack for infrared detectors that hide persons utilizing the wearable Warming Paste and Cooling Paste. By attaching these readily available temperature-controlled materials to the body, \textsc{HotCold} Block evades human eyes efficiently. Moreover, unlike existing methods that build adversarial patches with complex texture and structure features, \textsc{HotCold} Block utilizes an SSP-oriented adversarial optimization algorithm that enables attacks with pure color blocks and explores the influence of size, shape, and position on attack performance. Extensive experimental results in both digital and physical environments demonstrate the performance of our proposed \textsc{HotCold} Block. \emph{Code is available: \textcolor{magenta}{https://github.com/weihui1308/HOTCOLDBlock}}.<br />Comment: Accepted to AAAI 2023

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2212.05709
Document Type :
Working Paper