Learning optical image encryption scheme based on CycleGAN

被引：0
|
作者：
Li J.-Q. [1 ,2 ]
Zhou J. [1 ,2 ]
Di X.-Q. [1 ,2 ,3 ]
机构：
[1] School of Computer Science and Technology, Changchun University of Science and Technology, Changchun
[2] Jilin Province Key Laboratory of Network and Information Security, Changchun University of Science and Technology, Changchun
[3] Information Center, Changchun University of Science and Technology, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2021年 / 51卷 / 03期
关键词：
Cycle-consistent adversarial networks; Deep learning; Double random phase encoding; Image security; Optical image encryption;
D O I：
10.13229/j.cnki.jdxbgxb20200521
中图分类号：
学科分类号：
摘要：
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. © 2021, Jilin University Press. All right reserved.
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页码：1060 / 1066
页数：6
相关论文
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