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Erschienen in: Neural Computing and Applications 11/2019

22.05.2018 | Original Article

A chaotic image encryption algorithm based on 3-D bit-plane permutation

verfasst von: Zhi-hua Gan, Xiu-li Chai, Dao-jun Han, Yi-ran Chen

Erschienen in: Neural Computing and Applications | Ausgabe 11/2019

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Abstract

There are two shortcomings existing in the current color image encryption. One is that high correlation between R, G, B components of the original image may be neglected, the other is that the encryption has little relationship with the plain image, and then it is vulnerable to be broken. In order to solve these two problems and present secure and effective image encryption scheme, we introduce a novel chaos-based image encryption algorithm for color images based on three-dimensional (3-D) bit-plane permutation. In the proposed algorithm, the color plain image is firstly converted to 24 bit planes by RGB splitting and bit plane decomposition, next three-dimensional bit-plane permutation is performed on bit planes, position sequences for permutation are obtained from the 3D Chen chaotic system, and then the three confused components are gotten. Secondly, three key matrices are generated by a 1D chaotic system and a multilevel discretization method, and finally, the color cipher image is obtained by diffusing the confused components using key matrices. The SHA 256 hash function value of the plain image is obtained and combined with the given parameters to calculate the parameters and initial values of the chaotic system, so that the proposed scheme highly depends on the plain image and it may effectively withstand known-plaintext and chosen-plaintext attacks. Simulation results and security analyses demonstrate that our algorithm not only has good encryption effect, but can also resist against common attacks, so it is reliable to be applied for image secure communications.

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Metadaten
Titel
A chaotic image encryption algorithm based on 3-D bit-plane permutation
verfasst von
Zhi-hua Gan
Xiu-li Chai
Dao-jun Han
Yi-ran Chen
Publikationsdatum
22.05.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3541-y

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