A visually secure image encryption algorithm based on block compressive sensing and deep neural networks

A novel visually secure image encryption algorithm is proposed by combining compressive sensing and deep neural networks. To achieve a tradeoff between the visual quality and the reconstruction quality in different scenarios, a multi-channel sampling network structure is constructed to provide different compression performances. Then, the pre-encrypted compressed image is embedded into the host image by the IWT embedding strategy in the sampling network. During the matrix reconstruction process, a deep reconstruction network is employed for full image denoising, significantly reducing the impact of block artifacts and resulting in reconstructed images with higher visual quality. Simulation results indicate that the present algorithm can reconstruct images efficiently with high quality at very low sampling rates, while greatly preserving the advantages of speed and learning ability of deep neural networks.