SEEK: Detecting GPS Spoofing via a Sequential Dashcam-Based Vehicle Localization Framework

Peng Jiang, Hongyi Wu, Yanxiao Zhao, Dan Zhao, Chunsheng Xin

Abstract: GPS spoofing is a great threat to the safety of transportation systems as well as other systems that rely on GPS for navigation. This paper proposes a novel computer vision based approach for GPS spoofing detection, termed SEquential dashcam-based vEhicle localization frameworK (SEEK). SEEK utilizes vehicle dashcam images to identify a vehicle's true location and detects possible GPS spoofing attacks through verifying if the reported GPS locations of the vehicle are correct. However, it is nontrivial to use dashcam images for vehicle localization due to multiple challenges caused by real-world driving, including the complicated lighting/weather conditions, season/timing variations of the images, large blockage ratio in the images, and varying driving speeds. SEEK features a unique design with novel schemes to address complicated lighting/weather conditions, transform images to align with season changes, reduce blockage, and adopt a sequential image matching scheme. The performance evaluation shows that SEEK significantly outperforms the previous GPS spoofing detection scheme, and achieves a detection accuracy of up to 94%.