An Enhanced Image Encryption Framework Integrating Hyper-Chaotic Maps with the Serpent Block Cipher for Secure Medical and Biometric Data Transmission
DOI:
https://doi.org/10.29304/jqcsm.2026.18.22711Keywords:
Image Encryption, Serpent Algorithm, Block Cipher, Cryptography, CBC Mode, PythonAbstract
The secure transmission of sensitive e-images, especially medical records and biometric templates, remains a vital challenge because of the characteristics of images, including high adjacency correlation between the pixels, large volumes of data, and vulnerability to statistical attacks. This study presents an enhanced multi stage confusion–diffusion image encryption framework employing a four-dimensional (4D) hyper-chaotic map with the Serpent block cipher (via Cipher Block Chaining (CBC) mode). The confusion stage employs a 4D hyperchaotic system that permutes pixels through a variation at the pixel-level to destabilize the spatial temporal structure of the original image, subsequently leading to a diffusion stage where 32-rounds of the Serpent 192 -bit cipher homogenizes the distribution of gray-levels over the entire image. The framework is wrapped in a modular three-layer software architecture: a User Interface Layer, a Logic Layer and a Cryptographic Core, and coded for implementation in Python with CustomTkinter-based graphical user interface. Experimental evaluation on standard test images and representative medical and biometric datasets shows excellent security performance: information entropy of 7.9996 (close to the theoretical maximum of 8.0), Number of Pixels Change Rate (NPCR) > 99.6%, a Unified Average Changing Intensity (UACI) ≈ 33.47%, and nearzero horizontal and vertical correlation coefficients. These results confirm that the proposed framework provides robust protection for clinical and biometric authentication contexts.
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Copyright (c) 2026 Abdulhadi Nadhim Mohsin Alzamili
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