Exploiting one-dimensional improved Chebyshev chaotic system and partitioned diffusion based on the divide-and-conquer principle for 3D medical model encryption

Yang Lu, Mengxin Gong, Zhihua Gan, Xiuli Chai, Lvchen Cao and Binjie Wang

Chaos, Solitons & Fractals, 2023, vol. 171, issue C

Abstract: As the digital age progresses, 3D models have become increasingly popular in various fields, such as medicine, engineering, and the metaverse. One of the main benefits of using 3D models is their ability to provide more realistic and concrete representations. In the medical field specifically, 3D medical models have proven to be very useful in assisting with diagnosis and treatment. However, this also increases the risk of tampering during transmission, making the protection of 3D model data crucial. In this paper, an encryption algorithm for 3D medical models is proposed. First, a one-dimensional improved Chebyshev chaotic system (1D-ICCS) is designed for generating pseudo-random sequences based on the Chebyshev chaotic system and memristor model, and experimental analyses prove that it has better chaotic performance in several aspects. Further, the chaotic system is used to design a permutation-diffusion framework for 3D models. Therein, a permutation based on combinatorial chaotic indexes (PBCCI) is presented to disrupt data correlation, followed by a partitioned diffusion based on the divide-and-conquer principle (PDBDCP) that separates the floating-point 3D model data into integer and decimal parts and diffuses them using different methods. Experimental results show that the proposed method can resist typical attacks for encrypting 3D medical models and that the ciphertext information entropy reaches 7.999.

Keywords: Chaos; 3D model; Image encryption; Partitioned diffusion (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:171:y:2023:i:c:s0960077923003508

DOI: 10.1016/j.chaos.2023.113449

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