论文标题
Invisibilitee：带有TEE的人跟踪系统的角度不足
InvisibiliTee: Angle-agnostic Cloaking from Person-Tracking Systems with a Tee
论文作者
论文摘要
经过对人体跟踪系统引起的隐私问题的调查，我们提出了一种黑盒对抗攻击方法，该方法称为Invisibilitee。该方法学习了可打印的对抗性图案，适用于T恤，这些T恤在人体跟踪系统前的物理世界中抓起佩戴者。我们设计了一种角度不足的学习方案，该方案利用了时尚数据集的分割和几何翘曲过程，因此生成的对抗模式可有效从所有摄像机角度和看不见的黑盒检测模型欺骗人探测器。数字环境和物理环境中的经验结果表明，随着Invisibilitee On，人跟踪系统检测佩戴者的能力大大降低。
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.
