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Open Access

Image Encryption Algorithm Based on a Hybrid Model of Novel Memristive Hyperchaotic Systems, DNA Coding, and Hash Functions

School of Electrical Engineering, Xinjiang University, Urumqi 830017, China
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Abstract

The design of a chaotic image encryption algorithm plays an essential role in enhancing information and communication security. The performance of such algorithms is intricately linked to the complexity of the chaotic sequence and the underlying encryption algorithm. To additionally enhance the complexity of hyperchaotic systems, this study presents a novel construction of a Five-Dimensional (5D) memristive hyperchaotic system through the introduction of the flux-controlled memristor model. The system’s dynamic characteristics are examined through various analytical methods, including phase portraits, bifurcation diagrams, and Lyapunov exponent spectra. Accordingly, the sequences produced by the hyperchaotic system, which passed the National Institute of Standards and Technology (NIST) test, are employed to inform the creation of a novelty image encryption technique that combines hash function, Deoxyribonucleic Acid (DNA) encoding, logistic, and Two-Dimensional Hyperchaotic Map (2D-SFHM). It improves the sensitivity of key and plaintext images to image encryption, expands the algorithm key space, and increases the complexity of the encryption algorithm. Experimental findings and analysis validate the exceptional encryption capabilities of the novel algorithm. The algorithm exhibits a considerable key space 2512, and the ciphertext image demonstrates an information entropy of 7.9994, with inter-pixel correlation approaching zero, etc., showcasing its resilience against different types of attacks on images.

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Complex System Modeling and Simulation
Pages 303-319

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Cite this article:
Chong Z, Wang C, Zhang H, et al. Image Encryption Algorithm Based on a Hybrid Model of Novel Memristive Hyperchaotic Systems, DNA Coding, and Hash Functions. Complex System Modeling and Simulation, 2024, 4(3): 303-319. https://doi.org/10.23919/CSMS.2024.0015

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Received: 26 March 2024
Revised: 10 May 2024
Accepted: 08 June 2024
Published: 30 September 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).