A Fuzzy Adaptive ZNN Model and Its Application on Remote Sensing Image Security

As most of the digital image encryption algorithms are dependent with matrix theory, matrix equations are widely used in digital image encryption algorithms. Matrix inversion is a basic matrix equation operation, and how to effectively solve the matrix inversion problem has drawn considerable attention. However, previous works mainly focused on the static matrix inversion, and they are not suitable for modern time-varying problems solving. Zeroing neural network (ZNN) is a emerging systematic approach for solving time-varying problems, and it has been successfully applied in the dynamic matrix inversion. In order to enhance the performance of the existing models, this paper constructs a novel fuzzy adaptive ZNN (FA-ZNN) model, which incorporates segmented activation functions and an adaptive fuzzy dynamic convergence factor. Based on the constructed FA-ZNN model, a novel time-varying Hill cipher (NTVHC) with chaotic sequences scrambling and encryption for remote sensing images encryption is proposed. The proposed NTVHC algorithm utilizes the tangential-delay elliptic reflection cavity system (TD-ERCS) to generate two chaotic sequences for pixels displacement scrambling while constructing dynamic key matrix for image encryption. The superior performance of the constructed FA-ZNN model for dynamic key matrix inversion are validated by mathematical analysis and comparative simulation results with other models. Additionally, in subsequent practical applications, the model is used during the NTVHC decryption, which further verifies the feasibility of the proposed NTVHC encryption algorithm and the promising application prospects of the FA-ZNN model.