4.6 Article

An efficient symmetric image encryption algorithm based on an intertwining logistic map

期刊

NEUROCOMPUTING
卷 251, 期 -, 页码 45-53

出版社

ELSEVIER
DOI: 10.1016/j.neucom.2017.04.016

关键词

Image privacy; Image encryption; Chaotic map; Chaotic cryptography

资金

  1. National Natural Science Foundation of China [61602124]
  2. Natural Science Foundations of Guangdong Province of China [2016A030310333, 2015A030313614, 2015A030313620]
  3. Science AMP
  4. Technology Planning Projects of Zhanjiang City of China [2015B01098, 20151301051]
  5. Program for Scientific Research Start-up Funds of Guangdong Ocean University of China
  6. Special Funding Program for Excellent Young Scholars of Guangdong Ocean University of China [HDYQ2017006]

向作者/读者索取更多资源

Differing from the traditional permutation-diffusion architecture, an efficient symmetric image encryption algorithm is designed in this paper. To resolve the issue of low sensitivity of a plain-image, which is measured by a unified averaged changing intensity and the number of changing pixel rate, pre-modular operation is proposed to be used for pre-processing a classical encryption algorithm. Because of the invariance of pixel summation in the plain-image before and after pixel positions exchange, the keystream used for the permutation operation is designed to be dependent on the plain-image. Since for most encryption algorithms it is hard to implement the diffusion operation in relation with the plain-image dependence, a self-adaptive encryption scheme is proposed as a remedy. Consequently, the keystream for diffusion becomes dependent on the plain-image, which still satisfies the requirement as a symmetric cipher. To that end, by taking full advantage of the chaotic intertwining logistic map and its unpredictability, a new cipher with pre-modular, permutation and diffusion is designed for image encryption, which is then simulated and tested. Numerical experiments and security analysis demonstrate that the new cipher is secure enough for image communication over the open network. (C) 2017 Elsevier B.V. All rights reserved.

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