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图学学报

图学学报 ›› 2021, Vol. 42 ›› Issue (6): 891-898.DOI: 10.11996/JG.j.2095-302X.2021060891

• 图像处理与计算机视觉 • 上一篇    下一篇

基于细胞神经网络和并行压缩感知的 图像加密算法

  

  1. 1. 长安大学信息工程学院，陕西 西安 710064； 2. 大连海事大学信息科学技术学院，辽宁 大连 116026； 3. 哈尔滨师范大学物理与电子工程学院，黑龙江 哈尔滨 150025
  • 出版日期:2022-01-18 发布日期:2022-01-18
  • 基金资助:
    国家自然科学基金项目(61701043)；陕西省科技计划项目(2020JM-220，2020JQ-351) 

Image encryption algorithm based on cellular neural network and parallel compressed sensing

  1. 1. School of Information Engineering, Chang’an University, Xi’an Shaanxi 710064, China; 2. School of Information Science and Technology, Dalian Maritime University, Dalian Liaoning 116026, China; 3. School of Physics and Electronic Engineering, Harbin Normal University, Harbin Heilongjiang 150025, China
  • Online:2022-01-18 Published:2022-01-18
  • Supported by:
    The National Natural Science Foundation of China (61701043); The Shaanxi Province Science and Technology Program (2020JM-220, 2020JQ-351) 

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摘要： 基于细胞神经网络(CNN)和并行压缩感知(CS)提出了一种高安全性的非可视化图像加密算法， 旨在提高现有加密算法的信息传输效率以及减少存储空间。首先明文图像的小波系数经过阈值处理和索引置乱 后，利用受控的部分哈达玛矩阵对其进行并行压缩，接着执行费雪耶兹行列置乱和加模操作，然后再将部分加 密图像分割并通过最低有效位(LSB)嵌入算法随机地隐藏到剩余加密图像的 alpha 通道中生成最终的类噪声密 文图像，具有超混沌特性的 CNN 所产生的伪随机序列用于构造置乱、扩散以及受控测量矩阵。最后，通过一 系列的安全性分析表明，该算法具有很高的传输效率和安全性。

关键词: 图像加密, 压缩感知, 细胞神经网络, LSB 嵌入, 安全分析

Abstract:  A high-security non-visual image encryption algorithm based on cellular neural network (CNN) and parallel compressed sensing (CS) was proposed, aiming to improve the information transmission efficiency and reduce the storage space of existing encryption algorithms. First, the wavelet coefficients of plain image were processed by thresholding and index confusion, and compressed by the key-controlled partial Hadama matrix in parallel. Next, the Fisher-Yates confusion and modular arithmetic were performed. Then the partial encrypted image was segmented and randomly hidden into the alpha channel of remaining encrypted image by the least significant bit (LSB) embedding algorithm, thereby generating the final noise-like cipher image. In this scheme, the pseudo-random sequences generated by CNN with hyperchaotic properties were employed to construct the scrambling, diffusion, and key-controlled measurement matrix. Eventually, a series of security analyses indicated that the proposed image encryption algorithm is of high efficiency and security in transmission. 

Key words:  image encryption, compressed sensing, cellular neural network, LSB embedding, security analysis