摘要
为克服光学图像加密方法受光学器件性能限制和随机相位掩膜板制造工艺复杂的问题,提出了一种基于循环生成对抗网络(CycleGAN)的学习型光学图像加密方案。首先,使用经典双随机相位编码加密明文样本图像,构造出明文图像-密文图像训练集。然后,将其作为循环生成对抗网络的输入,自动学习光学图像加密的加密特性,训练得到光学图像加密学习模型。最后,利用仿真实验对使用CycleGAN训练的加密模型生成的图像进行加密解密性能测试。数据分析表明,该模型能够有效保护图像信息的安全和较好地恢复密文图像,学习型光学加密模型具有加密性能不受光学加密器件限制的优点,可以实现批量图像的快速加密。
To overcome the problem that the effect of optical image encryption is limited by the processing technology of the optical encryption devices and the manufacturing process of the random phase mask is complicated,in this paper,an optical image encryption learning scheme based on cycle-consistent adversarial networks(CycleGAN)is proposed.Firstly,the classic double random phase encoding is used to encrypt the plain image to generate a plain-cipher training set.Secondly,the training set is input to CycleGAN to automatically learn the encryption characteristics of optical image encryption to obtain an optical image encryption learning model.Finally,encryption and decryption performance tests are carried out by simulation experiments on the images generated by the learning encryption mechanism of CycleGAN.Data analysis shows that this scheme can effectively protect the security of image information and recover ciphertext images well.In addition,the encryption performance is not limited by optical encryption equipment,which can realize the rapid encryption of batches of images.
作者
李锦青
周健
底晓强
LI Jin-qing;ZHOU Jian;DI Xiao-qiang(School of Computer Science and.Technology,Changchun University of Science and Technology,Changchun 130022,China;Jilin Province Key Laboratory of Network and Information Security,Changchun University of Science and Technology,Changchun 130022,China;Information Center,Changchun University of Science and Technology,Changchun 130022,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2021年第3期1060-1066,共7页
Journal of Jilin University:Engineering and Technology Edition
基金
国家重点研发计划项目(2018YFB1800303)
吉林省自然科学基金项目(20190201188JC)
吉林省高等教育教学改革研究项目(JLLG685520190725093004)。
关键词
图像安全性
循环生成对抗网络
深度学习
光学图像加密
双随机相位编码
image security
cycle-consistent adversarial networks
deep learning
optical image encryption
double random phase encoding
