InvisibiliTee: Angle-agnostic Cloaking from Person-Tracking Systems with a Tee

• URL: http://arxiv.org/abs/2208.06962v1
• Date: Mon, 15 Aug 2022 01:32:09 GMT
• Title: InvisibiliTee: Angle-agnostic Cloaking from Person-Tracking Systems with a Tee
• Authors: Yaxian Li, Bingqing Zhang, Guoping Zhao, Mingyu Zhang, Jiajun Liu, Ziwei Wang, and Jirong Wen
• Abstract summary: We propose a black-box adversarial attack method on state-of-the-art human detection models called InvisibiliTee. The method learns printable adversarial patterns for T-shirts that cloak wearers in the physical world in front of person-tracking systems.
• Score: 46.611395940966915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: After a survey for person-tracking system-induced privacy concerns, we propose a black-box adversarial attack method on state-of-the-art human detection models called InvisibiliTee. The method learns printable adversarial patterns for T-shirts that cloak wearers in the physical world in front of person-tracking systems. We design an angle-agnostic learning scheme which utilizes segmentation of the fashion dataset and a geometric warping process so the adversarial patterns generated are effective in fooling person detectors from all camera angles and for unseen black-box detection models. Empirical results in both digital and physical environments show that with the InvisibiliTee on, person-tracking systems' ability to detect the wearer drops significantly.

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