Raymond Muller, Yanmao Man, Z. Berkay Celik, Ming Li, Ryan M. Gerdes
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引用次数: 4
Abstract
Modern autonomous systems rely on both object detection and object tracking in their visual perception pipelines. Although many recent works have attacked the object detection component of autonomous vehicles, these attacks do not work on full pipelines that integrate object tracking to enhance the object detector's accuracy. Meanwhile, existing attacks against object tracking either lack real-world applicability or do not work against a powerful class of object trackers, Siamese trackers. In this paper, we present AttrackZone, a new physically-realizable tracker hijacking attack against Siamese trackers that systematically determines valid regions in an environment that can be used for physical perturbations. AttrackZone exploits the heatmap generation process of Siamese Region Proposal Networks in order to take control of an object's bounding box, resulting in physical consequences including vehicle collisions and masked intrusion of pedestrians into unauthorized areas. Evaluations in both the digital and physical domain show that AttrackZone achieves its attack goals 92% of the time, requiring only 0.3-3 seconds on average.
现代自主系统在其视觉感知管道中依赖于目标检测和目标跟踪。尽管最近的许多研究都攻击了自动驾驶汽车的目标检测组件,但这些攻击并不适用于集成目标跟踪以提高目标检测器准确性的完整管道。同时,现有的针对对象跟踪的攻击要么缺乏现实世界的适用性,要么不能针对一种强大的对象跟踪器——暹罗跟踪器。在本文中,我们提出了AttrackZone,这是一种新的物理可实现的跟踪器劫持攻击,针对Siamese跟踪器,系统地确定可用于物理扰动的环境中的有效区域。AttrackZone利用Siamese Region Proposal Networks的热图生成过程来控制物体的边界框,从而导致包括车辆碰撞和遮挡行人进入未经授权区域在内的物理后果。在数字和物理领域的评估表明,AttrackZone在92%的时间内实现了攻击目标,平均只需要0.3-3秒。