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PSO-based image encryption scheme using modular integrated logistic exponential map

delete2024-03-01
delete75
PRE
AI
Ö
Ömer Koçak
U
Uğur Erkan *
A
Abdurrahim Toktaş
S
Suo Gao
DOI:10.1016/j.eswa.2023.121452delete
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摘要

摘要

En 中文
Image encryption (IE) has been essential for internet-based storing and transferring in recent years. Effective chaotic systems play a crucial role in IE schemes which widely depend on chaotic maps and keys. However, the existing chaotic maps suffer from low performance and narrow chaotic ranges, and they utilize casual key generation approaches rather than optimum keys. In this study, an IE scheme based on key optimization using particle swarm optimization (PSO) algorithm and a novel modular integrated logistic exponential (MILE) map is presented. The chaotic performance of the MILE map is validated across a comparison with the existing maps thorough measurements such as LE, SE, 0-1 Test, and PE with means values of 12.0000, 2.1866, 0.9981, and 0.9998, respectively. The key is optimized on a small part of the image to be encrypted instead of the whole image that is employed by the existing works. That is why the IE scheme is faster than those works. Afterward, the PSO-based IE scheme is undergone reliable crypto-analyses and attacks. It is also verified through a comparison with the existing IE schemes. The proposed IE scheme is the best owing to having exceptional mean values of key sensitivity 99.6117, variance 893.12, chi 2 222.4057, information entropy 7.9994, NPCR 99.6090, and UACI 33.4662. Thanks to the MILE map, the PSO-based IE scheme demonstrates excellent numerical and visual encryption results.
Keyword:
Chaotic map
Image encryption
Particle swarm optimization
Logistic map
Exponential map

期刊

Expert Systems with Applications 封面图
Expert Systems with Applications
IF:
7.5
论文数:
3.0W
被引数:
10.2W

机构

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harbin institute of technology
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8.0W
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被引数: 66
K
Karamanoglu Mehmetbey University
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860
论文数: 1.0K
被引数: 14
K
Konya Technical University
学者数:
812
论文数: 834
被引数: 673
A
Ankara University
学者数:
8.5K
论文数: 7.2K
被引数: 5.9K
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