A self-reversible image encryption algorithm utilizing a novel chaotic map

This paper introduces an innovative self-reversible image encryption algorithm designed to optimize resource utilization and enhance the efficiency of managing encryption and decryption processes in time-division multiplexing communication systems. By ingeniously integrating dual functionalities, th...

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Vydáno v:Nonlinear dynamics Ročník 113; číslo 7; s. 7351 - 7383
Hlavní autor: Li, Lizong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.04.2025
Springer Nature B.V
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ISSN:0924-090X, 1573-269X
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Shrnutí:This paper introduces an innovative self-reversible image encryption algorithm designed to optimize resource utilization and enhance the efficiency of managing encryption and decryption processes in time-division multiplexing communication systems. By ingeniously integrating dual functionalities, the algorithm significantly reduces the requirement for hardware and software resources. The proposed algorithm combines self-reversible shifting, self-reversible permutation, and two complementary reversible diffusion processes to ensure overall self-reversibility while enhancing security. A novel enhanced cosine-sine-logistic chaotic map is introduced to generate the pseudo-random sequences essential for encryption. Comprehensive performance evaluations, including analyses of chaotic trajectories, bifurcation diagrams, Lyapunov exponents, sample entropy, the 0–1 test, and NIST assessments, confirm the superior dynamic characteristics of the chaotic model. The image encryption algorithm also exhibits excellent performance in key metrics such as key sensitivity, histogram uniformity, correlation of adjacent pixels, local Shannon entropy, resistance to differential attacks, key space size, and robustness against various attacks. Comparisons with existing algorithms underscore its significant advantages in both encryption performance and efficiency. This study offers new insights and a robust solution for securing image data in real-world network environments.
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-024-10726-4