  Digital image watermarking is the process of embedding and extracting
watermark covertly on a carrier image. Incorporating deep learning networks
with image watermarking has attracted increasing attention during recent years.
However, existing deep learning-based watermarking systems cannot achieve
robustness, blindness, and automated embedding and extraction simultaneously.
In this paper, a fully automated image watermarking system based on deep neural
networks is proposed to generalize the image watermarking processes. An
unsupervised deep learning structure and a novel loss computation are proposed
to achieve high capacity and high robustness without any prior knowledge of
possible attacks. Furthermore, a challenging application of watermark
extraction from camera-captured images is provided to validate the practicality
as well as the robustness of the proposed system. Experimental results show the
superiority performance of the proposed system as comparing against several
currently available techniques.
