Beta Chaotic Map Based Image Encryption Using Genetic Algorithm

Abstract

In this paper, an efficient image encryption technique using beta chaotic map, nonsubsampled contourlet transform, and genetic algorithm is proposed. Initially, the nonsubsampled contourlet transform is utilized to decompose the input image into subbands. The beta chaotic map is used to develop pseudo-random key that encrypts the coefficients of subbands. However, it requires certain parameters to encrypt these coefficients. A multiobjective fitness function for genetic algorithm is designed to find the optimal parameter of beta chaotic map. The inverse of nonsubsampled contourlet transform is performed to obtain a ciphered image. The performance of the proposed technique is compared with recently developed well-known meta-heuristic based image encryption techniques. Experimental results reveal that the proposed technique provides better computational speed and high encryption intensity. The comparative analyses show effectiveness of the proposed image encryption technique.