%0 Journal Article %A JIANG Donghua %A RONG Xianwei %A SHEN Ziyi %A WANG Weiya %T Visual image encryption algorithm based on Hopfield chaotic neural network and compressive sensing %D 2021 %R 10.11772/j.issn.1001-9081.2020121942 %J Journal of Computer Applications %P 2893-2899 %V 41 %N 10 %X 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. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2020121942