3D medical model encryption based on five-dimensional hyperchaotic systems with 3D Arnold transform and selectable multiple spiral arrangements
Abstract
In the era of digitization and informatization, 3D models are used in a variety of fields, notably in medicine, engineering and design, seamlessly integrating into people's daily lives. Particularly within the medical field, 3D models enjoy extensive utilization. Consequently, this paper introduces a novel 3D medical model encryption algorithm, designated as 3D3A-SMA. First, a five-dimensional hyperchaotic system with multiple stability is applied to the encryption process of this algorithm. In this encryption algorithm, the vertex data of the 3D model is divided into integer and fractional parts and different diffusion methods are applied to them, respectively. A 3D Arnold spiral subregion diffusion based on chaotic system (3ASDC) is proposed to diffuse the integer part, and then, a selectable multiple spiral arrangement subregion diffusion (SMASD) is proposed to diffuse the fractional part. The simulation results and performance analysis show that the proposed encryption algorithm can accurately encrypt and decrypt the 3D medical model. The numerical results in the performance analysis are very close to the ideal values, with the information entropy of the ciphertext and each dimension reaching 7.998. In addition, the correlation within the ciphertext is also very close to the ideal value of 0.000, The algorithm also shows strong resistance to common attacks.
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Index Terms
- 3D medical model encryption based on five-dimensional hyperchaotic systems with 3D Arnold transform and selectable multiple spiral arrangements
Index terms have been assigned to the content through auto-classification.
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Kluwer Academic Publishers
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Publication History
Published: 21 October 2024
Accepted: 02 October 2024
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