Covert Attack and Detection Through Deep Neural Network on Vision-Based Navigation Systems of Multi-Agent Autonomous Vehicles

Autonomous vehicles are prone to worms of intelligent cyber-attacks that can use novel deep neural networks to adapt themselves to hosts and remain stealthy for a long period of time. This paper introduces a new, vision-based, covert attack and detection method, called VCAD-GAN, which can be applied to the navigation system of various autonomous vehicles. VCAD-GAN injects a faulty signal into the actuator channel to drive the vehicle out of the lane. To conceal the deviation effects, VCAD-GAN manipulates the camera output using a generative adversarial network such that the vehicle is shown on the road’s center-line and aligned with it. The generator of VCAD-GAN reconstructs a synthesized top view of the road, while the discriminator classifies it as authentic or fake and sends feedback to the generator. A hybrid adversary detection system is also developed for VCAD-GAN using a customized deep neural network, global positioning system data, and an offline map. To evaluate the performance of VCAD-GAN, various 3D simulations were conducted. The simulation results show the validity and effectiveness of the proposed methods.