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一种新型五维三叶混沌吸引子及其在图像加密中的应用。

A Novel Five-Dimensional Three-Leaf Chaotic Attractor and Its Application in Image Encryption.

作者信息

Wang Tao, Song Liwen, Wang Minghui, Chen Shiqiang, Zhuang Zhiben

机构信息

College of Computer Science, Sichuan University, Chengdu 610000, China.

School of Advanced Materials and Mechatronic Engineering, Hubei Mizu University, Enshi 445000, China.

出版信息

Entropy (Basel). 2020 Feb 21;22(2):243. doi: 10.3390/e22020243.

Abstract

This paper presents a novel five-dimensional three-leaf chaotic attractor and its application in image encryption. First, a new five-dimensional three-leaf chaotic system is proposed. Some basic dynamics of the chaotic system were analyzed theoretically and numerically, such as the equilibrium point, dissipative, bifurcation diagram, plane phase diagram, and three-dimensional phase diagram. Simultaneously, an analog circuit was designed to implement the chaotic attractor. The circuit simulation experiment results were consistent with the numerical simulation experiment results. Second, a convolution kernel was used to process the five chaotic sequences, respectively, and the plaintext image matrix was divided according to the row and column proportions. Lastly, each of the divided plaintext images was scrambled with five chaotic sequences that were convolved to obtain the final encrypted image. The theoretical analysis and simulation results demonstrated that the key space of the algorithm was larger than 10 that had strong key sensitivity. It effectively resisted the attacks of statistical analysis and gray value analysis, and had a good encryption effect on the encryption of digital images.

摘要

本文提出了一种新型的五维三叶混沌吸引子及其在图像加密中的应用。首先,提出了一种新的五维三叶混沌系统。从理论和数值两方面分析了该混沌系统的一些基本动力学特性,如平衡点、耗散性、分岔图、平面相图和三维相图。同时,设计了一个模拟电路来实现该混沌吸引子。电路仿真实验结果与数值仿真实验结果一致。其次,使用卷积核对五个混沌序列分别进行处理,并根据行和列的比例对明文图像矩阵进行划分。最后,将划分后的每个明文图像用五个经过卷积的混沌序列进行置乱,得到最终的加密图像。理论分析和仿真结果表明,该算法的密钥空间大于10,具有较强的密钥敏感性。它有效地抵抗了统计分析和灰度值分析的攻击,对数字图像加密具有良好的加密效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d7/7516698/5dab08c6a5e2/entropy-22-00243-g001a.jpg

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