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Color image encryption using non-dominated sorting genetic algorithm with local chaotic search based 5D chaotic map

机译:基于非混沌排序遗传算法和基于局部混沌搜索的5D混沌图的彩色图像加密

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摘要

The secure key generation is the predominant requirement of an image encryption. Chaotic maps are often considered by the researchers for secure key generation. However, chaotic maps suffer from hyper-tuning issue because the requirement of initial parameters. Therefore, an integrated non-dominated sorting genetic algorithm and local chaotic search based image encryption technique is proposed to tune the hyper-parameters of 5D chaotic map (TFCM). To implement TFCM, initially, the input image is decomposed into sub-bands using a dual-tree complex wavelet transform (DTCWT). These sub-bands are then diffused using the secret key obtained from the optimized 5D chaotic map. Finally, the inverse DTCWT is applied to obtain the final encrypted image. However, TFCM is computationally extensive for images with a larger size. Therefore, a parallel implementation of TFCM is also considered. Experimental analyses show that TFCM outperforms the competitive techniques in terms of NPCR, entropy, PSNR, and UACI by 0.9572%, 1.1576%, 1.0373%, and 1.0854%, respectively.
机译:安全密钥生成是图像加密的主要要求。研究人员经常考虑使用混沌地图来生成安全密钥。然而,由于初始参数的要求,混沌映射存在超调问题。因此,提出了一种集成的非支配排序遗传算法和基于局部混沌搜索的图像加密技术来调整5D混沌映射(TFCM)的超参数。为了实现TFCM,首先,使用双树复数小波变换(DTCWT)将输入图像分解为子带。然后,使用从优化的5D混沌图获得的秘密密钥对这些子带进行扩散。最后,逆DTCWT用于获得最终的加密图像。但是,TFCM对于较大尺寸的图像在计算上是广泛的。因此,还考虑了TFCM的并行实现。实验分析表明,TFCM在NPCR,熵,PSNR和UACI方面分别优于竞争技术,分别为0.9572%,1.1576%,1.0373%和1.0854%。

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