Training digital hologram watermarking deep neural network considering hologram distributions

Ju-Won Lee; Jae-Eun Lee; Young-Ho Seo; Dong-Wook Kim

doi:10.1117/12.2590972

13 March 2021 Training digital hologram watermarking deep neural network considering hologram distributions

Ju-Won Lee, Jae-Eun Lee, Young-Ho Seo, Dong-Wook Kim

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 117662C (2021) https://doi.org/10.1117/12.2590972
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

Digital Hologram is a very high-value-added image content, whose intellectual property should be protected for its distribution. Therefore its watermarking technology has become much important. This paper is to propose a training scheme of a deep neural network to perform a digital watermarking for a digital hologram. We construct various training datasets according to the distribution of holograms and train them on the proposed network. The network consists of preprocessing networks for both host data and watermark, watermark embedding network, and watermark extraction network, all of which consist of simple convolutional neural network (CNN) modules. The dataset is constructed with the digital holograms from JPEG Pleno and they are classified by distribution of the hologram pixel values. The network is trained with each of the classified holograms. With each of the trained weight sets, the invisibility and watermark extraction rate are calculated and compared with the results from testing the test dataset that has not been used for training. From the result, we explain the effects of the data distribution of the training dataset on the invisibility and robustness of the watermarking to present the best training scheme for a digital watermarking of a digital hologram.

Citation Download Citation

Ju-Won Lee, Jae-Eun Lee, Young-Ho Seo, and Dong-Wook Kim "Training digital hologram watermarking deep neural network considering hologram distributions", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 117662C (13 March 2021); https://doi.org/10.1117/12.2590972

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