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}.

翻译：混沌系统因其对初始条件的敏感性、遍历性及复杂动力学特性，在现代图像加密中发挥着关键作用。然而，现有许多基于混沌的加密方法存在脆弱性，例如置乱与扩散不充分、伪随机特性欠佳等。本文提出了一种新颖的加密框架Kun-IE，旨在解决这些问题。该框架包含两项关键贡献：一是设计了二维正弦-余弦π超混沌映射（2D-SCPHM），其具有更宽的混沌范围和更优的伪随机序列生成能力；二是提出了Kun-SCAN这一新型置乱策略，能显著降低像素相关性，从而增强对统计攻击的抵抗能力。Kun-IE具有灵活性，支持任意尺寸图像的加密。实验结果与安全性分析表明，该框架能有效抵抗多种密码分析攻击，为安全的图像通信提供了可靠的解决方案。代码可通过此\href{https://github.com/QuincyQAQ/Elevating-Medical-Image-Security-A-Cryptographic-Framework-Integrating-Hyperchaotic-Map-and-GRU}{链接}获取。