Digital image encryption utilizing high-dimensional cellular neural networks and lower-upper triangular decomposition of matrix.

At present, significant progress has been made in the research of image encryption, but there are still some issues that need to be explored in key space, password generation and security verification, encryption schemes, and other aspects. Aiming at this, a digital image encryption algorithm was developed in this paper. This algorithm integrates six-dimensional cellular neural network with generalized chaos to generate pseudo-random numbers to generate the plaintext-related ciphers. The initial image matrix is transformed into L-matrix and U-matrix through Lower-Upper decomposition. These matrices are then encrypted simultaneously with distinct cipher sequences. The algorithm's feasibility and security are demonstrated through comprehensive encryption simulations and performance analysis. The paper's contributions include i) the cellular neural network and an innovative chaos approach to develop a new ciphers scheme; ii) the image decomposition encryption effectively shorten the cipher length and reduce interception risks during transmission; iii) the frequent application of nonlinear transforms enhances the structural complexity of the cryptosystem and fortifies the security of the algorithm. Compared to existing algorithms, the paper achieves a novel image decomposition encryption mode with comprehensive advantages. This mode is expected to be applied in image communication security.