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Remote sensing image encryption algorithm utilizing 2D Logistic memristive hyperchaotic map and SHA-512

delete2024-04-23
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PRE
AI
Q
Qiang Lai *
Y
Yuan Liu
L
Liang Yang
DOI:10.1007/s11431-023-2584-ydelete
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摘要

摘要

En 中文
The two-dimensional Logistic memristive hyperchaotic map (2D-LMHM) and the secure hash SHA-512 are the foundations of the unique remote sensing image encryption algorithm (RS-IEA) suggested in this research. The proposed map is formed from the improved Logistic map and the memristor, which has wide phase space and hyperchaotic range and is exceptionally excellent to be utilized in specific applications. The proposed image algorithm uses the permutation-assignment-diffusion structure. Permutation generates two position matrices in a progressive manner to achieve an efficient random exchange of pixel positions, assignment is carried through on the image pixels of the permutated image to entirely remove the original image information, strengthening the relationship between permutation and diffusion, and loop diffusion in two different directions can use subtle changes of pixels to affect the whole plane. The random key and plain-image SHA-512 hash values are used to produce an additional key, which is then utilized to figure out the permutation parameters and the initial value of a chaotic map. The experimental results with the average NPCR = 99.6094% (NPCR: number of pixels change rate), average UACI = 33.4638% (UACI: unified average changing intensity), 100% pass rate of the targets in the test set, the average correlation coefficient is 0.00075, and the local information entropy is 7.9025, which shows that the algorithm is able to defend against a variety of illegal attacks and provide more trustworthy protection than some of the existing state-of-the-art algorithms.
Keyword:
chaos
memristive hyperchaotic map
remote sensing
SHA-512
image encryption
RS-IEA

期刊

Science China-Technological Sciences 封面图
Science China-Technological Sciences
IF:
4.9
论文数:
5.0K
被引数:
9.9K

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East China Jiaotong University
学者数:
4.1K
论文数: 2.9K
被引数: 2.9K
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