A dual-image encryption scheme for sensitive region of traffic image using public key cryptosystem and novel chaotic map
Tingyu An, Tao Gao, Ting Chen, Donghua Jiang
Abstract
Intelligent transport systems (ITS) extensively employ traffic images for traffic flow analysis, accident detection and other tasks. However, these images contain sensitive privacy information, including license plates and facial features of drivers, which form sensitive regions (SRs) that are vulnerable to unauthorized exposure. Due to computational limits and security risks caused by key transmission, existing encryption techniques face challenges in efficiency and complex key management. To address these issues, we first introduce a novel three-dimensional composite chaotic map (3D-CCM) validated by various metrics to ensure superior complexity and randomness. Then, an efficient dual-image encryption scheme based on the 3D-CCM(DIES-CCM) that focuses on the SRs within traffic images is proposed. In the encryption phase, SRs from two traffic images undergo 1D permutation, cellular automaton-based public key encryption, and bidirectional diffusion, achieving parallel and region-specific encryption that significantly improves efficiency. Additionally, the cellular automaton-based public key cryptosystem ensures secure key transmission and simplifies key management. Experimental results demonstrate the effectiveness of the DIES-CCM in enhancing privacy protection and its strong resistance to cryptographic attacks, providing a practical solution for safeguarding sensitive information in ITS.