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

December 12, 2022 ยท Declared Dead ยท ๐Ÿ› AAAI Conference on Artificial Intelligence

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Authors Hui Wei, Zhixiang Wang, Xuemei Jia, Yinqiang Zheng, Hao Tang, Shin'ichi Satoh, Zheng Wang arXiv ID 2212.05709 Category cs.CV: Computer Vision Citations 55 Venue AAAI Conference on Artificial Intelligence Repository https://github.com/weihui1308/HOTCOLDBlock}} Last Checked 1 month ago
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}}.
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