A Time-Varying Hill Cipher for Dynamic Image Cryptography

Nowadays, information security becomes increasing important, and cryptography is an indispensable part of information security. Typically, the encryption algorithms are implemented through performing various operations on plaintext with a secret key to achieve information hiding. Based on the existing tangent-delay ellipse reflecting cavity-map system (TD-ERCS) chaotic system and the traditional Hill cipher (THC) with a time-invariant key matrix, a time-varying Hill cipher (TVHC) with a time-variant key matrix is proposed in this work. As an effective method in solving time-varying problems, the zeroing neural network (ZNN) is used to effective find the time-variant inversion key matrix (TVIKM) for the TVHC decryption process. Moreover, a novel fix-time convergence fuzzy ZNN (NFCF-ZNN) with superior convergence and robustness is constructed for quickly solving the TVIKM of the TVHC decryption process. The convergence and robustness of NFCF-ZNN for solving TVKIM in the absence and presence of noise are both demonstrated through rigorous mathematical derivation and comparative simulation experiments. Additionally, the successful simulation experiments of the proposed TVHC in grayscale and RGB color images encryption and decryption further validates its effectiveness in practical applications.