Multiobjective evolutionary optimization techniques based hyperchaotic map and their applications in image encryption

Chaotic-based image encryption approaches have attracted great attention in the field of information security. The properties of chaotic maps such as randomness and sensitivity have given new ways to develop efficient encryption approaches. But chaotic maps require initial parameters to develop random sequences. The selection of these parameters is a tedious task. To obtain the optimal initial parameters, evolutionary optimization approaches have been utilized in image encryption. Therefore, in this paper, a hyper-chaotic map is optimized using a multiobjective evolutionary optimization approach. A dual local search based multiobjective optimization (DLS-MO) is used to obtain the optimal parameters of a hyper-chaotic map and encryption factors. Then, using optimal parameters, a hyper-chaotic map develops the secret keys. These secret keys are then used to perform permutation and diffusion on a plain image to develop the encrypted image. To perform encryption, permutation–permutation–diffusion–diffusion architecture is adopted for better confusion and diffusion. Experimental results show that the proposed approach provides better performance in comparison to existing competitive approaches.