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Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack.

Ji-Won Kang1, Jae-Eun Lee2, Jang-Hwan Choi1

  • 1Department of Electronic Materials Engeering, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul 01897, Korea.

Sensors (Basel, Switzerland)
|August 10, 2021
PubMed
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This study introduces a novel deep neural network for robust digital hologram watermarking. The method embeds and extracts watermarks while simulating attacks for enhanced security and invisibility.

Area of Science:

  • Computer Vision
  • Digital Image Processing
  • Cryptography

Background:

  • Digital holograms are susceptible to unauthorized access and manipulation.
  • Existing watermarking techniques may lack robustness against various attacks.
  • Deep learning offers potential for advanced image security solutions.

Purpose of the Study:

  • To propose a novel deep neural network (DNN) based method for embedding and extracting watermarks in digital holograms.
  • To enhance the robustness and invisibility of watermarking through integrated attack simulation and holographic reconstruction within the DNN.
  • To develop a re-training strategy for improving robustness against specific attacks.

Main Methods:

  • A three-sub-network architecture for digital hologram watermarking.
Keywords:
convolution neural network (CNN)deep neural network (DNN)digital hologramdigital watermarktraining dataset

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  • Integration of attack simulation and holographic reconstruction within the DNN for simultaneous training of invisibility and robustness.
  • A proposed network training methodology utilizing hologram and reconstruction data.
  • Iterative re-training based on robustness analysis against various attacks.

    • Main Results:

    • The proposed DNN effectively embeds and extracts watermarks from digital holograms.
    • The integrated attack simulation enhances watermark robustness against diverse threats.
    • Quantitative evaluation demonstrates reliable performance against multiple attack scenarios.
    • The re-training strategy successfully improves robustness based on attack analysis.

    Conclusions:

    • The developed DNN-based watermarking technique offers a reliable solution for securing digital holograms.
    • Simultaneous training of invisibility and robustness within the network architecture is effective.
    • The proposed method provides a robust and adaptable approach to digital hologram watermarking.