Visual Deception: Demonstrating Spoofing Attacks on Autonomous Vehicle Cameras

Abstract

Autonomous vehicles (AVs) are the cornerstone of the future intelligent transportation systems. The AVs are intelligent vehicles with sophisticated sensing capabilities powered by advanced artificial intelligence. Among the various sensors, cameras constitute the most significant sensor that enables the AV to perceive the environment in real time to safely navigate. However, the safety of AVs depends upon the accuracy of object detection using cameras, and any attack on the camera may cause incidents. This paper demonstrates how a simple hardware setup can enable spoofing attacks on the AV camera to inject fake objects into the camera video feed to fool the AV perception system. Our experiments show that such attacks can be deployed physically by attaching a simple hardware setup and the attacker can spoof objects at any instance without accessing the camera feed. Lastly, the paper provides insights on how to mitigate such attacks in AVs.