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：在本工作中，我们研究了无人飞行器在基于感知的控制中所受隐蔽攻击的脆弱性。为引导分析，我们考虑了两种特定任务：（i）地面车辆跟踪，以及（ii）四旋翼无人机在移动地面车辆上的垂直起降。具体而言，我们提出了一种持续攻击传感器测量值和相机图像的方法，旨在导致控制性能下降（例如，任务失败），同时保持隐蔽性（即不被部署的异常检测器发现）。与现有主要依赖深度神经网络对小输入扰动脆弱性的攻击（例如，在图像中添加小补丁和/或噪声）不同，我们证明通过改变地面车辆着陆标记的图像以及适当伪造传感数据，可以设计出隐蔽而有效的攻击。我们在Gazebo三维机器人仿真器中展示了攻击的有效性。