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Article

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

1
Department of Electronic Materials Engeering, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul 01897, Korea
2
OLED Team Associate, Siliconworks, Baumoe-ro, Seocho-gu, Seoul 06763, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Robert Sitnik
Sensors 2021, 21(15), 4977; https://doi.org/10.3390/s21154977
Received: 2 July 2021 / Revised: 17 July 2021 / Accepted: 19 July 2021 / Published: 22 July 2021
This paper proposes a method to embed and extract a watermark on a digital hologram using a deep neural network. The entire algorithm for watermarking digital holograms consists of three sub-networks. For the robustness of watermarking, an attack simulation is inserted inside the deep neural network. By including attack simulation and holographic reconstruction in the network, the deep neural network for watermarking can simultaneously train invisibility and robustness. We propose a network training method using hologram and reconstruction. After training the proposed network, we analyze the robustness of each attack and perform re-training according to this result to propose a method to improve the robustness. We quantitatively evaluate the results of robustness against various attacks and show the reliability of the proposed technique. View Full-Text
Keywords: digital hologram; digital watermark; deep neural network (DNN); training dataset; convolution neural network (CNN) digital hologram; digital watermark; deep neural network (DNN); training dataset; convolution neural network (CNN)
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MDPI and ACS Style

Kang, J.-W.; Lee, J.-E.; Choi, J.-H.; Kim, W.; Kim, J.-K.; Kim, D.-W.; Seo, Y.-H. Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack. Sensors 2021, 21, 4977. https://doi.org/10.3390/s21154977

AMA Style

Kang J-W, Lee J-E, Choi J-H, Kim W, Kim J-K, Kim D-W, Seo Y-H. Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack. Sensors. 2021; 21(15):4977. https://doi.org/10.3390/s21154977

Chicago/Turabian Style

Kang, Ji-Won, Jae-Eun Lee, Jang-Hwan Choi, Woosuk Kim, Jin-Kyum Kim, Dong-Wook Kim, and Young-Ho Seo. 2021. "Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack" Sensors 21, no. 15: 4977. https://doi.org/10.3390/s21154977

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