A privacy-preserving license plate encryption scheme based on an improved YOLOv8 image recognition algorithm

Abstract

With the rapid development of green smart cities, urban intelligence has also brought new challenges, particularly in protecting the privacy information of vehicles within the city. To address this issue, we propose a novel image encryption scheme to ensure the security of image transmission. This method captures vehicle images through roadside surveillance cameras and uses an improved YOLOv8 algorithm to identify sensitive vehicle information in real time, which is then securely transmitted to the city’s traffic management system. To safeguard the data, we employ a combination of the Rabbit Competition scrambling algorithm and a custom diffusion kernel to perform real-time encryption on the identified image regions, protecting privacy-sensitive areas. Experimental results show that our method improves accuracy by 1.53% and average precision by 1.4% on the test set compared to the baseline model, indicating a substantial enhancement in detection accuracy. Additionally, the encryption scheme demonstrates a larger key space, improved robustness, and significantly enhanced anti-attack capabilities, confirming its effectiveness in protecting vehicle information in smart city environments.