With the increasing reliance on intelligent transportation systems, securing traffic images against unauthorized access and tampering has become a critical concern. The work introduces a novel 4D hybrid chaotic system, integrating the memristive Rucklidge system and a discrete nonlinear map and an image encryption scheme as its application. The proposed encryption framework ensures high security and resistance against cryptographic attacks by leveraging chaotic dynamics for both permutation and diffusion processes. The encryption scheme primarily has three phases: (i) the generation of the Quadrant Hybrid Chaotic Matrix (QHCM), a dynamically structured chaotic matrix divided into four quadrants, each governed by different chaotic equations; (ii) concentric ring permutation, where traffic image pixels are rearranged within concentric rings using QHCM to enhance security; and (iii) QHCM-based chain diffusion, ensuring a nonlinear and spatially distributed transformation of pixel values by processing rings from the outermost region towards the center. The number of permutation and diffusion rounds is dynamically determined based on the discrete evolution of the proposed chaotic system. Extensive experimental analysis demonstrates the robustness of the encryption scheme in terms of statistical, differential properties, key space analysis and randomness. In particular, the method achieves a Net Pixel Change Rate (NPCR) of 99.62% and Unified Average Changing Intensity (UACI) of 50.09%, key space Formula: see text ,with a highest entropy 7.999769 outperforming other existing systems. The proposed approach is well-suited for real-time encryption of traffic surveillance images, ensuring secure transmission and storage in intelligent transportation networks.
Sarra et al. (Fri,) studied this question.