Vulnerability of Unmanned Aerial Vehicles under Stealthy Perception-based Attacks

Abstract

Unmanned aerial vehicles (UAVs) have been widely used in various domains of industry and people's daily life. However, the vulnerability of UAVs has been underestimated and compromised UAV systems could put autonomous missions at risk. This paper studies the vulnerability of UAVs and implements an effective and stealthy perception-based attack. This stealthy attack does not rely on the vulnerability of deep neural networks but makes use of fake image transformations and sensor signal manipulations. In particular, two specific autonomous missions are investigated: ($i$) ground vehicle tracking (GVT), and ($ii$) vertical take-off and landing (VTOL) of a quadcopter on a moving ground vehicle. Experimental results with Gazebo simulations show that this stealthy attack causes significant deviation from the UAV's designated trajectories, and remains undetected from state-of-art intrusion detectors.