A single exposure optical image watermarking framework based on deep learning (DL) is proposed in this paper, and original watermark image information can be reconstructed from only single-frame watermarked hologram by using an end-to-end network with high-quality. First, the single exposure watermarked hologram is acquired with our presented phase-shifted interferometry based optical image watermarking (PSOIW) frame, and then all holograms and corresponding watermark images are constructed to the train datasets for the learning of an end-to-end conditional generative adversarial network (cGAN), finally retrieved the watermark image well with the trained cGAN network using only one hologram. This DL-based method greatly reduces the recording or transmitting data burden by 1/4 compared with our presented PSOIW technique, and may provide a new way for the real-time 3D image/video security applications. The feasibility and security of the proposed method are demonstrated by the optical experiment results.

中文翻译：

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使用cGAN网络的单次曝光光学图像水印

本文提出了一种基于深度学习（DL）的单曝光光学图像水印框架，利用高质量的端到端网络，仅从单帧水印全息图就可以重建原始水印图像信息。首先，使用我们提出的基于相移干涉术的光学图像水印（PSOIW）框架获取单次曝光水印全息图，然后将所有全息图和相应的水印图像构建到训练数据集中，以学习端到端条件生成对抗网络（cGAN），最终仅使用一个全息图就可以使用训练有素的cGAN网络很好地检索水印图像。与我们提出的PSOIW技术相比，这种基于DL的方法大大减少了1/4的记录或传输数据负担，并可以为实时3D图像/视频安全应用程序提供新的方式。光学实验结果证明了该方法的可行性和安全性。