There has been a recent surge in interest in autonomous robots and vehicles. From the Google self-driving car, to autonomous delivery robots, to hobbyist UAVs, there is a staggering variety of proposed deployments for autonomous vehicles. Ensuring that such vehicles can plan and execute routes safely is crucial.
The key insight of our paper is that the sensors that autonomous vehicles use to navigate represent a vector for adversarial control. With direct knowledge of how sensor algorithms operate, the adversary can manipulate the victim’s environment to form an implicit control channel on the victim. We craft an attack based on this idea, which we call a sensor input spoofing attack.
We demonstrate a sensor input spoofing attack against the popular Lucas-Kanade method for optical flow sensing and characterize the ability of an attacker to trick optical flow via simulation. We also demonstrate the effectiveness of our optical flow sensor input spoofing attack against two consumer-grade UAVs, the AR.Drone 2.0 and the APM 2.5 ArduCopter. Finally, we introduce a method for defending against such an attack on opticalflow sensors, both using the RANSAC algorithm and a more robust weighted RANSAC algorithm to synthesize sensor outputs.