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

Key words:  image encryption, compressed sensing, cellular neural network, LSB embedding, security analysis