Steganographic optical image encryption based on single-pixel imaging and an untrained neural network

被引:19
|
作者
Lin, Shanshan [1 ]
Wang, Xiaogang [1 ,2 ]
Zhu, Angang [1 ]
Xue, Jidong [1 ]
Xu, Bijun [2 ]
机构
[1] Zhejiang A&F Univ, Dept Opt Engn, Hangzhou 311300, Peoples R China
[2] Zhejiang Univ Sci & Technol, Dept Appl Phys, Hangzhou 310023, Peoples R China
基金
中国国家自然科学基金;
关键词
DEEP-LEARNING APPROACH; GHOST; CODE;
D O I
10.1364/OE.467708
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
We propose a steganographic optical image encryption based on single-pixel imaging (SPI) and an untrained neural network. In this encryption scheme, random binary illumination patterns are projected onto a secret image and light intensities reflected from the image are then detected by a bucket detector (BD). To enhance the security of collected secret data, a steganographic approach is introduced in this method, which implements data hiding with a SPI system using encoded illumination patterns. A non-secret image is illuminated with a sequence of encoded patterns that were generated from the scrambled measurements of secret image, and sequential cyphertext data can be obtained by collecting the diffraction data with the BD. Different from traditional SPI-based encryption schemes, an untrained neural network is adopted as a SPI-encrypted image processor, which allows to reduce time spent on data preparation and reconstruct the secret images with high quality. Both computer simulations and optical experiments are carried out to demonstrate the feasibility of the method.
引用
收藏
页码:36144 / 36154
页数:11
相关论文
共 50 条
  • [21] Single-Pixel Image Classification via Nonlinear Optics and Deep Neural Network
    Kumar, Santosh
    Bu, Ting
    Zhang, He
    Huang, Irwin
    Huang, Yu-Ping
    2021 CONFERENCE ON LASERS AND ELECTRO-OPTICS (CLEO), 2021,
  • [22] DON based single-pixel imaging
    Wang, Zhirun
    Zhao, Wenjing
    Zhai, Aiping
    He, Peng
    Wang, Dong
    OPTICS EXPRESS, 2021, 29 (10) : 15463 - 15477
  • [23] Single-pixel imaging using a recurrent neural network combined with convolutional layers
    Hoshi, Ikuo
    Shimobaba, Tomoyoshi
    Kakue, Takashi
    Ito, Tomoyoshi
    OPTICS EXPRESS, 2020, 28 (23): : 34069 - 34078
  • [24] MWIR image deep denoising reconstruction based on single-pixel imaging
    Yang, Shuowen
    Qin, Hanlin
    Yan, Xiang
    Zhao, Dong
    Zeng, Qingjie
    OPTICS COMMUNICATIONS, 2025, 574
  • [25] Optical multiple-image compression-encryption via single-pixel Radon transform
    Wu, Jingjing
    Li, Siwei
    APPLIED OPTICS, 2020, 59 (31) : 9744 - 9754
  • [26] Optical Encryption Using Attention-Inserted Physics-Driven Single-Pixel Imaging
    Yu, Wen-Kai
    Wang, Shuo-Fei
    Shang, Ke-Qian
    SENSORS, 2024, 24 (03)
  • [27] Dual-mode optical microscope based on single-pixel imaging
    Rodriguez, A. D.
    Clemente, P.
    Tajahuerce, E.
    Lancis, J.
    OPTICS AND LASERS IN ENGINEERING, 2016, 82 : 87 - 94
  • [28] ON EVALUATION OF IMAGE QUALITY IN NONPARAXIAL SINGLE-PIXEL IMAGING
    Mundrys, K.
    Orlov, S.
    Kizevicius, P.
    Minkevicius, L.
    Valusis, G.
    LITHUANIAN JOURNAL OF PHYSICS, 2023, 63 (03): : 113 - 121
  • [29] Optical frequency comb profilometry based on a single-pixel phase imaging
    Makhtar, Nabila
    Quang Duc Pham
    Mizutani, Yasuhiro
    Hayasaki, Yoshio
    INTERFEROMETRY XVIII, 2016, 9960
  • [30] Optical Logic Gate Operations With Single-Pixel Imaging
    Jiao, Shuming
    Feng, Jun
    Zhang, Liwen
    Wu, Daixuan
    Shen, Yuecheng
    IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, 2023, 29 (02)