A color image encryption scheme based on cellular neural networks and linear feedback shift registers

Abstract

In this paper, a cellular neural network (CNN) chaotic system is constructed and the multiple stability of the system and its rich chaotic properties are confirmed by studying the effect of parameters on the system, coexisting attractors, and offset boosting behavior. As linear feedback shift registers (LFSR) can be applied to cryptography, this paper applies LFSR to generate encrypted key matrices to enhance the randomness of encryption algorithms. Based on CNN and LFSR, a new color image encryption algorithm is designed by combining DNA coding and bit-plane decomposition with high bit-plane Zigzag dislocation changes. Experimental results and security tests show that the algorithm is highly secure and resistant to a variety of common attacks, such as differential attacks, cropping attacks, and noise attacks.