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A novel DNA tree-based chaotic image encryption algorithm

delete2024-06-01
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Moatsum Alawida *
DOI:10.1016/j.jisa.2024.103791delete
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摘要

摘要

En 中文
For confidential transmission of information over open channels, image encryption algorithms offer a vital method to safeguard content. Traditional ciphers often prove inefficient for large, correlated content like digital images. To address this, DNA and digital chaos are utilized in the creation of numerous image ciphers. Most DNA chaotic image ciphers utilize DNA bases for substitution and chaotic maps for permutation operations. This paper introduces a novel approach wherein the secret key is generated by a DNA tree, providing complete control over all components of the image cipher. A new chaotic state machine map (CSMM) is proposed, incorporating a finite state machine and a one-dimensional (1D) perturbed logistic chaotic map controlled by a DNA state transition table. The CSMM enhances security while maintaining efficient implementation. The DNA tree is employed to generate a DNA table consisting of 256 values representing DNA bases. This table is used to convert digital images into DNA bases and chaotic points into DNA bases. Chaotic DNA bases facilitate permutation operations, while the DNA table is utilized to generate a DNA S-box for substitution operations. The encryption process involves two rounds of permutations and substitutions, coupled with XOR operations, using all DNA bases in the digital image. The resulting cipher effectively encrypts digital images of various sizes and types. Experimental results demonstrate that the proposed cipher generates noise-like images and withstands rigorous security tests, including differential attack analysis and entropy measurements, all accomplished in a short amount of time.
Keyword:
Chaotic map
Image encryption
Logistic chaotic map
DNA bases
Chaotic cipher

期刊

Journal of Information Security and Applications 封面图
Journal of Information Security and Applications
IF:
3.7
论文数:
1.9K
被引数:
4.9K

机构

A
Abu Dhabi University
学者数:
1.2K
论文数: 1.3K
被引数: 1.7K
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