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Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347406/ https://www.ncbi.nlm.nih.gov/pubmed/34372214 http://dx.doi.org/10.3390/s21154977 |
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author | Kang, Ji-Won Lee, Jae-Eun Choi, Jang-Hwan Kim, Woosuk Kim, Jin-Kyum Kim, Dong-Wook Seo, Young-Ho |
author_facet | Kang, Ji-Won Lee, Jae-Eun Choi, Jang-Hwan Kim, Woosuk Kim, Jin-Kyum Kim, Dong-Wook Seo, Young-Ho |
author_sort | Kang, Ji-Won |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8347406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83474062021-08-08 Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack Kang, Ji-Won Lee, Jae-Eun Choi, Jang-Hwan Kim, Woosuk Kim, Jin-Kyum Kim, Dong-Wook Seo, Young-Ho Sensors (Basel) Article 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. MDPI 2021-07-22 /pmc/articles/PMC8347406/ /pubmed/34372214 http://dx.doi.org/10.3390/s21154977 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kang, Ji-Won Lee, Jae-Eun Choi, Jang-Hwan Kim, Woosuk Kim, Jin-Kyum Kim, Dong-Wook Seo, Young-Ho Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack |
title | Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack |
title_full | Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack |
title_fullStr | Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack |
title_full_unstemmed | Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack |
title_short | Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack |
title_sort | digital hologram watermarking based on multiple deep neural networks training reconstruction and attack |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347406/ https://www.ncbi.nlm.nih.gov/pubmed/34372214 http://dx.doi.org/10.3390/s21154977 |
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