摘要
本文将粒子群优化算法(PSO)与超混沌系统相结合,提出一种基于多目标优化的人脸图像加密方案.该方案通过PSO算法协同优化多项加密评估指标,包括相关关系、像素变化率(NPCR)、统一平均变化强度(UACI)和信息熵.首先,初始化混沌系统的控制参数,并采用SHA-256算法生成混沌系统的初始值,迭代生成高敏感性的随机序列;其次,利用随机序列执行像素置乱、扩散和行列置乱操作,生成初始加密人脸图像;然后,将加密人脸图像视为PSO算法的个体,通过迭代更新个体的位置优化考虑多项指标的适应性函数;最后,确定混沌系统的最优参数,并得到最佳的加密人脸图像.实验结果表明,本文的方法在信息熵、像素相关系数、NPCR和UACI方面的表现都优于主流方法,这说明本文所提方法具有更高的安全性.
In this paper,a particle swarm optimization algorithm(PSO)is combined with a hyperchaotic system to present a face image encryption scheme based on multi-objective optimization.The scheme co-optimises various encryption evaluation metrics,including pixel correlation,number of pixel change rate(NPCR),uniform average changing intensity(UACI)and information entropy through the PSO algorithm.Firstly,the control parameters of the chaotic system are initialized,and the initial values of the hyperchaotic system are generated using the SHA-256 algorithm to iteratively produce highly sensitive random sequences.Secondly,the random sequences are used to perform pixel permutation,diffusion,and row-column permutation operations,resulting in the initial encrypted face image.Then,the encrypted face image is treated as an individual of the PSO algorithm,and the fitness function considering multiple metrics is optimized by iteratively updating the individual’s position.Finally,the optimal parameters of the hyperchaotic system are determined,and the best encrypted face image is obtained.Experiments demonstrate that the proposed algorithm outperforms the mainstream methods in terms of information entropy,pixel correlation coefficient,NPCR,and UACI,which indicates that the proposed method has higher security.
作者
余锦伟
谢巍
张浪文
余孝源
YU Jin-wei;XIE Wei;ZHANG Lang-wen;YU Xiao-yuan(College of Automation Science and Technology,South China University of Technology,Guangzhou Guangdong 510640,China;Key Laboratory of Autonomous Systems and Networked Control,Ministry of Education,Guangzhou Guangdong 510640,China;Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong,Guangzhou Guangdong 510640,China;School of Physics and Telecommunication Engineering,South China Normal University,Guangzhou Guangdong 510006,China)
出处
《控制理论与应用》
北大核心
2025年第5期875-884,共10页
Control Theory & Applications
基金
广东省重点领域研究发展计划项目(2018B010108001)资助.
关键词
混沌系统
粒子群算法
图像加密
智能优化
人脸隐私保护
chaotic system
particle swarm optimization algorithm
image encryption
intelligent optimization
face privacy protection
