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

Sensors

Jang-Hwan Choi¹  Young-Ho Seo¹  Woosuk Kim¹  Jin-Kyum Kim¹  Ji-Won Kang¹  Dong-Wook Kim¹  Jae-Eun Lee² 

[1] Department of Electronic Materials Engeering, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul 01897, Korea;OLED Team Associate, Siliconworks, Baumoe-ro, Seocho-gu, Seoul 06763, Korea;

关键词: digital hologram;    digital watermark;    deep neural network (DNN);    training dataset;    convolution neural network (CNN);   

DOI  :  10.3390/s21154977

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown