基于Hopfield混沌神经网络和压缩感知的可视化图像加密算法

目前大多数的图像加密算法直接将明文图像加密成无视觉意义的密文图像，而这类密文图像在传输过程中容易被黑客发现从而受到各种攻击。针对上述问题，结合Hopfield混沌神经网络与压缩感知技术提出了一种具有视觉意义的图像加密算法。首先，利用二维离散小波变换对明文图像进行稀疏化；其次，通过压缩感知对经过阈值处理的稀疏矩阵进行加密和测量；然后，在量化的中间密文图像中加入随机数并进行Hilbert置乱和扩散操作；最后，将生成的类噪声密文图像通过最低有效位（LSB）替换来嵌入到载体图像中的Alpha通道以生成具有视觉意义的隐写图像。与现有的可视化图像加密算法相比，所提算法展现出非常好的视觉安全性、解密质量以及鲁棒性，表明其具有广泛的应用场景。

Visual image encryption algorithm based on Hopfield chaotic neural network and compressive sensing

At present, most image encryption algorithms directly encrypt the plaintext image into a ciphertext image without visual meaning, which is easy to be found by hackers during the transmission process and therefore subjected to various attacks. In order to solve the problem, combining Hopfield chaotic neural network and compressive sensing technology, a visually meaningful image encryption algorithm was proposed. Firstly, the two-dimensional discrete wavelet transform was used to sparse the plaintext image. Secondly, the sparse matrix after threshold processing was encrypted and measured by compressive sensing. Thirdly, the quantized intermediate ciphertext image was filled with random numbers, and Hilbert scrambling and diffusion operations were performed to the image. Finally, the generated noise-like ciphertext image was embedded into the Alpha channel of the carrier image though the Least Significant Bit (LSB) replacement to obtain the visually meaningful steganographic image. Compared with the existing visual image encryption algorithms, the proposed algorithm demonstrates very good visual security, decryption quality and robustness, showing that it has widely application scenarios.