Advanced genetic image encryption algorithms for intelligent transport systems

Abstract

Ensuring the security of sensitive or private information is crucial to prevent malicious tampering, especially in multimedia applications like intelligent transport systems (ITS), which are vital components of a smart city. These systems can be vulnerable to traffic management and rerouting techniques that manipulate the images captured by roadside units. To address this challenge, this paper introduces advanced image encryption algorithms designed specifically for securing image manipulation and transmission in roadside ITS units. Initially, a sequential version of the algorithm is proposed, demonstrating a high level of confusion achieved through the chosen coding method (chromosomal representation). This sequential approach results in maximum interference between the original image and its encrypted counterpart, with an entropy level of 7.95, nearing the optimal value of 8. To improve computational efficiency, three additional algorithms are presented, utilizing parallelization based on the islanding model, both with and without migrations. The algorithms are designed to enhance security by increasing confusion and incorporating genetic diffusion. The performance and security of these algorithms are evaluated using established methods such as information entropy, differential attack analysis, and key space analysis. Our algorithms have also shown a strong ability to maintain performance and robustness even in the presence of noise. Furthermore, they exhibit superior resistance to attacks compared to recent competitive approaches. In summary, the proposed algorithms offer robust protection against image manipulation and unauthorized access in roadside ITS units, thereby contributing to the overall security and reliability of smart city infrastructure.