In this work, we study vulnerability of unmanned aerial vehicles (UAVs) to stealthy attacks on perception-based control. To guide our analysis, we consider two specific missions: ($i$) ground vehicle tracking (GVT), and ($ii$) vertical take-off and landing (VTOL) of a quadcopter on a moving ground vehicle. Specifically, we introduce a method to consistently attack both the sensors measurements and camera images over time, in order to cause control performance degradation (e.g., by failing the mission) while remaining stealthy (i.e., undetected by the deployed anomaly detector). Unlike existing attacks that mainly rely on vulnerability of deep neural networks to small input perturbations (e.g., by adding small patches and/or noise to the images), we show that stealthy yet effective attacks can be designed by changing images of the ground vehicle's landing markers as well as suitably falsifying sensing data. We illustrate the effectiveness of our attacks in Gazebo 3D robotics simulator.

翻译：在这项工作中,我们研究了无人驾驶飞行器(无人驾驶飞行器)对感知控制进行隐形攻击的脆弱性。为了指导我们的分析,我们考虑了两个具体的任务:地面飞行器跟踪(GVT)($1美元)和移动地面飞行器上四分仪的垂直起飞和着陆(VTOL)(2美元),具体地说,我们引入了一种方法,在移动地面飞行器上不断攻击传感器测量和相机图像,以便控制性能退化(例如,不执行飞行任务),同时保持隐形(即,被部署的异常探测器未发现)。 与目前主要依靠深神经网络对小型输入干扰的脆弱性(例如,在图像中添加小补丁和/或噪音)进行的攻击不同,我们表明,可以通过改变地面飞行器着陆标记的图像以及适当伪造遥感数据来设计隐形但有效的攻击。我们说明了我们在加泽博3D机器人模拟器中的攻击的有效性。</s>