Anovel color image encryption algorithm based on a fractional-order discrete chaotic neural network and DNAsequence operations

Li-ping CHEN, Hao YIN, Li-guo YUAN, António M. LOPES, J. A. Tenreiro MACHADO, Ran-chao WU

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Front. Inform. Technol. Electron. Eng ›› 2020, Vol. 21 ›› Issue (6) : 866-879. DOI: 10.1631/FITEE.1900709
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Anovel color image encryption algorithm based on a fractional-order discrete chaotic neural network and DNAsequence operations

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Abstract

A novel color image encryption algorithm based on dynamic deoxyribonucleic acid (DNA) encoding and chaos is presented. A three-neuron fractional-order discrete Hopfield neural network (FODHNN) is employed as a pseudo-random chaotic sequence generator. Its initial value is obtained with the secret key generated by a fiveparameter external key and a hash code of the plain image. The external key includes both the FODHNN discrete step size and order. The hash is computed with the SHA-2 function. This ensures a large secret key space and improves the algorithm sensitivity to the plain image. Furthermore, a new three-dimensional projection confusion method is proposed to scramble the pixels among red, green, and blue color components. DNA encoding and diffusion are used to diffuse the image information. Pseudo-random sequences generated by FODHNN are employed to determine the encoding rules for each pixel and to ensure the diversity of the encoding methods. Finally, confusion II and XOR are used to ensure the security of the encryption. Experimental results and the security analysis show that the proposed algorithm has better performance than those reported in the literature and can resist typical attacks.

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Fractional-order discrete systems / Neural networks / Deoxyribonucleic acid (DNA) encryption / Color image encryption

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Li-ping CHEN, Hao YIN, Li-guo YUAN, António M. LOPES, J. A. Tenreiro MACHADO, Ran-chao WU. Anovel color image encryption algorithm based on a fractional-order discrete chaotic neural network and DNAsequence operations. Front. Inform. Technol. Electron. Eng, 2020, 21(6): 866‒879 https://doi.org/10.1631/FITEE.1900709

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2020 Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature
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