基于细胞神经网络和并行压缩感知的图像加密算法

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。

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

 A high-security non-visual image encryption algorithm based on cellular neural network (CNN) and parallelcompressed sensing (CS) was proposed, aiming to improve the information transmission efficiency and reduce thestorage space of existing encryption algorithms. First, the wavelet coefficients of plain image were processed bythresholding and index confusion, and compressed by the key-controlled partial Hadama matrix in parallel. Next, theFisher-Yates confusion and modular arithmetic were performed. Then the partial encrypted image was segmented andrandomly hidden into the alpha channel of remaining encrypted image by the least significant bit (LSB) embeddingalgorithm, 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, andkey-controlled measurement matrix. Eventually, a series of security analyses indicated that the proposed imageencryption algorithm is of high efficiency and security in transmission.