35. Visually Meaningful Image Encryption Algorithm Based on Parallel Compressive Sensing and Cellular Neural Network

At present, most image encryption algorithms protect the image by converting the original image into a visually meaningless noise-like image. However, the noise-like image can easily attract the attention of attackers, which increases the risk of being deciphered. Thus, a novel meaningful image encryption algorithm based on parallel compressive sensing and cellular neural network is proposed in this paper. In our scheme, the image is processed by three modules, namely, compression, encryption and hiding. Among them, the compression module is utilized to compress the plain images in parallel. After that, the encryption module converts the compressed image into a meaningless noise-like image by diffusion and scrambling. Finally, the hiding module realizes the visualization of the encrypted image by embedding the encrypted image into a carrier image, which helps reduce the interest of attacker in deciphering cipher images. Experimental results and analysis indicate that the proposed algorithm has satisfactory performance and can effectively protect image information from being leaked under the premise of reducing computational overhead.