We present a system for generating inconspicuous-looking textures that, when displayed in the physical world as digital or printed posters, cause visual object tracking systems to become confused. For instance, as a target being tracked by a robot's camera moves in front of such a poster, our generated texture makes the tracker lock onto it and allows the target to evade. This work aims to fool seldom-targeted regression tasks, and in particular compares diverse optimization strategies: non-targeted, targeted, and a new family of guided adversarial losses. While we use the Expectation Over Transformation (EOT) algorithm to generate physical adversaries that fool tracking models when imaged under diverse conditions, we compare the impacts of different conditioning variables, including viewpoint, lighting, and appearances, to find practical attack setups with high resulting adversarial strength and convergence speed. We further showcase textures optimized solely using simulated scenes can confuse real-world tracking systems.

本文提出了一种生成不引人注目的材质的系统，能够迷惑物体跟踪系统，比如通过使用各种优化策略生成对抗性材质，进而在物理世界中对机器人相机进行视觉混淆，实现物体规避目的。同时，作者比较了不同的条件变量，包括视点，光照以及外观，以寻找实用的高性能攻击设置。

欺骗视觉目标跟踪的物理对抗纹理