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一种基于改进型阿诺德变换和混沌脉冲耦合神经网络的新型图像加密算法。

A Novel Image Encryption Algorithm Based on Improved Arnold Transform and Chaotic Pulse-Coupled Neural Network.

作者信息

Ye Jinhong, Deng Xiangyu, Zhang Aijia, Yu Haiyue

机构信息

College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China.

Engineering Research Center of Gansu Province for Intelligent Information Technology and Application, Lanzhou 730070, China.

出版信息

Entropy (Basel). 2022 Aug 10;24(8):1103. doi: 10.3390/e24081103.

DOI:10.3390/e24081103
PMID:36010767
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9407545/
Abstract

Information security has become a focal topic in the information and digital age. How to realize secure transmission and the secure storage of image data is a major research focus of information security. Aiming at this hot topic, in order to improve the security of image data transmission, this paper proposes an image encryption algorithm based on improved Arnold transform and a chaotic pulse-coupled neural network. Firstly, the oscillatory reset voltage is introduced into the uncoupled impulse neural network, which makes the uncoupled impulse neural network exhibit chaotic characteristics. The chaotic sequence is generated by multiple iterations of the chaotic pulse-coupled neural network, and then the image is pre-encrypted by XOR operation with the generated chaotic sequence. Secondly, using the improved Arnold transform, the pre-encrypted image is scrambled to further improve the scrambling degree and encryption effect of the pre-encrypted image so as to obtain the final ciphertext image. Finally, the security analysis and experimental simulation of the encrypted image are carried out. The results of quantitative evaluation show that the proposed algorithm has a better encryption effect than the partial encryption algorithm. The algorithm is highly sensitive to keys and plaintexts, has a large key space, and can effectively resist differential attacks and attacks such as noise and clipping.

摘要

信息安全已成为信息和数字时代的一个焦点话题。如何实现图像数据的安全传输和安全存储是信息安全的一个主要研究重点。针对这一热点话题,为提高图像数据传输的安全性,本文提出了一种基于改进的阿诺德变换和混沌脉冲耦合神经网络的图像加密算法。首先,将振荡复位电压引入到非耦合脉冲神经网络中,使非耦合脉冲神经网络呈现出混沌特性。通过混沌脉冲耦合神经网络的多次迭代生成混沌序列,然后将生成的混沌序列与图像进行异或运算对图像进行预加密。其次,利用改进的阿诺德变换对预加密图像进行置乱,进一步提高预加密图像的置乱程度和加密效果,从而得到最终的密文图像。最后,对加密后的图像进行安全性分析和实验仿真。定量评估结果表明,所提算法比部分加密算法具有更好的加密效果。该算法对密钥和明文高度敏感,具有较大的密钥空间,能有效抵抗差分攻击以及噪声和裁剪等攻击。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/cf62153997fd/entropy-24-01103-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/92d7c739889a/entropy-24-01103-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/298c21c692d7/entropy-24-01103-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/15af0e3138bc/entropy-24-01103-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/0732fbb78e84/entropy-24-01103-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/5e5bbd0ce594/entropy-24-01103-g021a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/cf62153997fd/entropy-24-01103-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/92d7c739889a/entropy-24-01103-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/298c21c692d7/entropy-24-01103-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/15af0e3138bc/entropy-24-01103-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/0732fbb78e84/entropy-24-01103-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/5e5bbd0ce594/entropy-24-01103-g021a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb40/9407545/cf62153997fd/entropy-24-01103-g022.jpg

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