Elevating Medical Image Security: A Cryptographic Framework Integrating Hyperchaotic Map and GRU
Weixuan Li, Guang Yu, Quanjun Li, Junhua Zhou, Jiajun Chen, Yihang Dong, Mengqian Wang, Zimeng Li, Changwei Gong, Lin Tang, Xuhang Chen
TL;DR
This work tackles the security of medical image transmission by introducing Kun-IE, a cryptographic framework that leverages a novel 2D hyperchaotic map and GRU-based sequence generation to produce robust key streams. It combines a 2D Sin-Cos Pi Hyperchaotic Map (2D-SCPHM) with a GRU to generate high-quality chaotic sequences, a Knot-like Unique Novel-Scan (Kun-SCAN) for effective pixel permutation, and an XOR diffusion (XDA) mechanism to ensure strong diffusion; the system supports multi-image encryption and relies on five secret keys. The main contributions include the 2D-SCPHM map with enhanced chaotic range and ergodicity, the GRU-assisted chaotic sequence generator, and the Kun-SCAN permutation that substantially lowers pixel correlations; security analyses reveal a large effective key space of $2^{260}$, high NPCR and UACI values close to ideal, near-maximum information entropy, and strong resistance to statistical attacks, complemented by NIST SP 800-22 pass results. The framework achieves efficient encryption times and demonstrates robust performance on medical image datasets, indicating its practical applicability for secure medical image communication and privacy preservation.
Abstract
Chaotic systems play a key role in modern image encryption due to their sensitivity to initial conditions, ergodicity, and complex dynamics. However, many existing chaos-based encryption methods suffer from vulnerabilities, such as inadequate permutation and diffusion, and suboptimal pseudorandom properties. This paper presents Kun-IE, a novel encryption framework designed to address these issues. The framework features two key contributions: the development of the 2D Sin-Cos Pi Hyperchaotic Map (2D-SCPHM), which offers a broader chaotic range and superior pseudorandom sequence generation, and the introduction of Kun-SCAN, a novel permutation strategy that significantly reduces pixel correlations, enhancing resistance to statistical attacks. Kun-IE is flexible and supports encryption for images of any size. Experimental results and security analyses demonstrate its robustness against various cryptanalytic attacks, making it a strong solution for secure image communication. The code is available at this \href{https://github.com/QuincyQAQ/Elevating-Medical-Image-Security-A-Cryptographic-Framework-Integrating-Hyperchaotic-Map-and-GRU}{link}.