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Efficient image encryption using the Tinkerbell map in conjunction with linear feedback shift registers

delete2023-10-19
delete2
PRE
AI
P
Puneet Kumar Pal
D
Dhirendra Kumar *
V
Varun Agarwal
DOI:10.1007/s11042-023-17236-2delete
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摘要

摘要

En 中文
Images are considered crucial for conveying information due to their visualisation properties and capacity to hold a large amount of data. To safeguard image data from potential risks of information leakage, numerous image encryption algorithms have been developed. These algorithms often make use of chaotic maps, known for their high unpredictability, ergodicity, and sensitivity to parameters and initial values. This paper introduces a new chaos-based digital image encryption algorithm to protect digital images from unauthorised access or attacks. The algorithm heavily relies on permutation and diffusion processes. Specifically, it employs two rounds of pixel permutation using a Tinkerbell chaotic map sequence on the source image and two rounds of pixel diffusion using the Linear Feedback Shift Register and Logistic Map sequences on the permuted image. The encryption algorithm's effectiveness is thoroughly examined through various cryptanalysis techniques, including key space analysis, information entropy, correlation coefficient, differential attack, key sensitivity, histogram analysis, occlusion attack, noise attack, and encryption execution time. The experimental results are then compared with the most recent literature to demonstrate the algorithm's reliability and its ability to withstand diverse attacks. Notably, the proposed algorithm stands out as a secure and viable encryption solution, requiring less computational time than previous studies, thus making it a more practical option for real-world applications.
Keyword:
Image encryption
Chaos based cryptography
Logistic map
Tinkerbell map
Linear feedback shift register

期刊

Multimedia Tools and Applications 封面图
Multimedia Tools and Applications
IF:
3
论文数:
2.0W
被引数:
3.2W

机构

D
Delhi Technological University
学者数:
2.6K
论文数: 2.3K
被引数: 2.7K
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