Elevating Medical Image Security: A Cryptographic Framework Integrating Hyperchaotic Map and GRU

2025-10-13

AI Summary

Existing chaotic medical image encryption methods suffer from low initial-value sensitivity, weak pseudorandomness, high pixel correlation, and insufficient permutation/diffusion. To address these limitations, this paper proposes a novel encryption framework integrating a hyperchaotic map with a Gated Recurrent Unit (GRU). Specifically, we design a 2D Sin-Cos Pi hyperchaotic map with an expanded chaotic domain and combine it with the Kun-SCAN scrambling strategy to significantly reduce inter-pixel correlation. The map generates high-complexity pseudorandom sequences, while the GRU dynamically regulates the diffusion process, enabling size-invariant, efficient encryption/decryption. Experimental results demonstrate strong resistance to statistical and differential attacks, an extremely large key space (>2⁵¹²), and high computational efficiency (average 0.82 s for 512×512 images), making the scheme suitable for real-time secure transmission of medical imagery.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Privacy
• Reasoning under Uncertainty: Graphical Models

Application Category

• Security and Privacy: Applications of cryptography
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies

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}.

Problem

• Addresses vulnerabilities in chaos-based medical image encryption
• Enhances permutation and diffusion to resist statistical attacks
• Improves pseudorandom sequence generation for secure communication

Innovation

• Developed 2D Sin-Cos Pi Hyperchaotic Map for encryption
• Introduced Kun-SCAN permutation strategy reducing pixel correlation
• Created flexible framework supporting images of any size