A Robust Image Watermarking System Based on Deep Neural Networks
This addresses the need for secure and practical digital watermarking in applications like copyright protection, though it appears incremental by building on prior deep learning approaches.
The paper tackled the problem of creating a deep learning-based image watermarking system that simultaneously achieves robustness, blindness, and automation, resulting in a system that shows superior performance compared to existing techniques.
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.