Data tampering detection and recovery scheme based on multi-branch target extraction for internet of vehicles

Abstract

With the rapid development of new networks such as 5 G/6 G, the Internet of Vehicles has been given features such as hyper-connectivity and hyper-intelligence, promoting the implementation of new scenarios for autonomous vehicles. However, on the Internet of vehicles, vehicles use many cameras, radars and other sensors to sense the environment and execute instructions, facing security issues such as road data tampering and hijacking. Consequently, this paper presents a data tampering detection and recovery scheme based on multi-branch target extraction. Specifically, for road images collected by sensors, this paper presents a target extraction method based on multi-branch spatial feature pyramid blocks to obtain road salient targets. Then, a tamper detection and recovery algorithm based on interval mapping is presented. Once the image road target is tampered with, this method can quickly detect the tampering traces and restore them to achieve the authenticity and integrity of the road image on the Internet of Vehicles. The availability of the proposed scheme is verified through comparative experiments, and the performance is improved satisfactorily compared with other works.