A hybrid genetic algorithm and chaotic function model for image encryption

Abstract

The security of digital images has attracted much attention recently. In this study, a new method based on a hybrid model is proposed for image encryption. The hybrid model is composed of a genetic algorithm and a chaotic function. In the first stage of the proposed method, a number of encrypted images are constructed using the original image and the chaotic function. In the next stage, these encrypted images are used as the initial population for the genetic algorithm. In each stage of the genetic algorithm, the answer obtained from the previous iteration is optimized to produce the best-encrypted image. The best-encrypted image is defined as the image with the highest entropy and the lowest correlation coefficient among adjacent pixels. The use of genetic algorithms in image encryption has been attempted for the first time in this paper. Analyzing the results from the performed experiments, a high level of resistance of the proposed method against brute-force and statistical invasions is obviously illustrated. The obtained entropy and correlation coefficients of the method are approximately 7.9978 and -0.0009, respectively.