TY - GEN
T1 - SEEK
T2 - 21st IEEE International Conference on Pervasive Computing and Communications, PerCom 2023
AU - Jiang, Peng
AU - Wu, Hongyi
AU - Zhao, Yanxiao
AU - Zhao, Dan
AU - Xin, Chunsheng
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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%.
AB - 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%.
KW - GPs spoofing
KW - deep learning
KW - vehicle localization' image matching
UR - https://www.scopus.com/pages/publications/85153939234
UR - https://www.scopus.com/pages/publications/85153939234#tab=citedBy
U2 - 10.1109/PERCOM56429.2023.10099105
DO - 10.1109/PERCOM56429.2023.10099105
M3 - Conference contribution
T3 - 2023 IEEE International Conference on Pervasive Computing and Communications, PerCom 2023
SP - 71
EP - 80
BT - 2023 IEEE International Conference on Pervasive Computing and Communications, PerCom 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 March 2023 through 17 March 2023
ER -