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基于蔡氏电路和压缩感知的图像压缩加密方法
引用本文:马啸宇,张金生,李婷.基于蔡氏电路和压缩感知的图像压缩加密方法[J].系统工程与电子技术,2021,43(9):2407-2412.
作者姓名:马啸宇  张金生  李婷
作者单位:火箭军工程大学导弹工程学院, 陕西 西安 710025
基金项目:国家自然科学基金(61673017);中国博士后科学基金(2019M3643)
摘    要:针对当前信号采集传输高效性和安全性的实际需求, 提出了基于蔡氏电路和压缩感知的图像压缩加密方法。首先利用SHA-256算法生成原始信号的消息摘要; 其次利用摘要生成蔡氏电路的初始值; 再次利用蔡氏电路生成的混沌模拟信号经过8位量化采集和数字移位差分生成伪随机序列, 从而构建出混沌观测矩阵并对信号进行压缩采样; 最后将压缩后的信号通过生成的伪随机序列进行置乱加密和灰度加密, 生成压缩加密后的信号。实验结果表明, 该方法生成的混沌观测矩阵性能优于传统的随机观测矩阵, 同时能够有效提高信号的传输效率及安全性。

关 键 词:压缩感知  蔡氏电路  伪随机序列  图像加密  观测矩阵  
收稿时间:2020-07-22

Image compress and encryption method based on Chua's circuit and compressed sensing
Xiaoyu MA,Jinsheng ZHANG,Ting LI.Image compress and encryption method based on Chua's circuit and compressed sensing[J].System Engineering and Electronics,2021,43(9):2407-2412.
Authors:Xiaoyu MA  Jinsheng ZHANG  Ting LI
Institution:College of Missile Engineering, Rocket Force University of Engineering, Xi'an 710025, China
Abstract:Aiming at the actual requirements of high efficiency and security in signal acquisition and transmission, a signal compression encryption method based on Chua's circuit and compressive sensing is proposed. Firstty, the SHA-256 algorithm is used to generate a message digest of the original signal; secondly, the digest is used to generate the initial value of the Chua's circuit; The chaotic observation matrix is used to compress and sample the signal by the chaotic analog signal generated from the Chua's circuit which is chosen to generate a pseudo-random sequence through 8-bit quantization acquisition and digital shift difference. Finally, the compressed signal is scrambled and encrypted with the generated pseudo-random sequence to generate a compressed and encrypted signal. The experimental results show that the performance of the chaotic observation matrix generated by this method is better than that of the traditional random observation matrix, and it can effectively improve the signal transmission efficiency and security.
Keywords:compressed sensing  Chua's circuit  pseudo random sequence  image encryption  observation matrix  
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