Stealthy Perception-based Attacks on Unmanned Aerial Vehicles

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.