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April 19, 2026SHILAP Revista de lepidopterología0 citationsOpen Access

SMLIE: a SHA-512 seeded multi-layer image encryption algorithm for medical image security

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KMK. MahalakshmiSNSivakumar Nagarajan

Key Points

  • The research aims to enhance the security of medical images during transmission and storage using a new encryption algorithm.
  • Develop a multi-layer image encryption technique using SHA-512 for key generation.
  • Implement layers including Arnold scrambling, logistic map XOR, and DWT embedding.
  • Use MATLAB for visualizing and testing encrypted images in real time.
  • SMLIE achieves high entropy and effectiveness in encrypting medical images.
  • Demonstrated strong resistance to attacks with measures like NPCR and UACI.
  • Suitable for real-time applications in resource-constrained healthcare systems.

Abstract

Protection of medical images during transmission and storage is critical for ensuring patient confidentiality. To provide secure encryption for healthcare applications, this study proposes the SHA-512 Seeded Multi-Layer Image Encryption (SMLIE) algorithm. It generates a session-specific key by hashing a password and the timestamp of the system using SHA-512, from which the initial states and parameters of the chaotic maps are deterministically derived for reproducibility. The encryption procedure consists of seven layers: Arnold scrambling, logistic map XOR, SHA256 masking, adaptive salt injection, row-column permutation, DWT embedding, and Henon map-based XOR, all of which improve diffusion, entropy, and adaptability to various attacks. A MATLAB graphical user interface (GUI) is used to visualize the encrypted pixel data during validation. The experimental findings indicate a high level of entropy, NPCR, and UACI, demonstrating the effectiveness of the algorithm for encrypting medical images in real time, especially in resource-constrained environments. SMLIE is suitable for real-time healthcare systems in which both privacy and low latency are essential.

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Cite This Study

Mahalakshmi et al. (2026) studied this question.

synapsesocial.com/papers/69e470a4010ef96374d8d800https://doi.org/10.1080/09540091.2026.2658952
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