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.

中文翻译：

---

基于超混沌映射的多目标进化优化技术及其在图像加密中的应用

基于混沌的图像加密方法在信息安全领域引起了极大的关注。混沌映射的随机性和敏感性等特性为开发有效的加密方法提供了新的途径。但是混沌地图需要初始参数来开发随机序列。这些参数的选择是一项繁琐的工作。为了获得最佳初始参数，图像加密中采用了进化优化方法。因此，在本文中，使用多目标进化优化方法来优化超混沌地图。基于双局部搜索的多目标优化 (DLS-MO) 用于获得超混沌映射和加密因子的最佳参数。然后，使用最佳参数，超混沌映射开发密钥。然后使用这些密钥对普通图像执行排列和扩散以开发加密图像。为了执行加密，采用置换-置换-扩散-扩散架构以更好地混淆和扩散。实验结果表明，与现有的竞争方法相比，所提出的方法提供了更好的性能。