A Robust Image Watermarking System Based on Deep Neural Networks

TL;DR

This paper introduces a fully automated deep neural network-based image watermarking system that achieves high robustness and capacity without prior attack knowledge, validated through camera-captured image experiments.

Contribution

It proposes an unsupervised deep learning framework with a novel loss function for robust, blind watermarking without prior attack information.

Findings

Outperforms existing watermarking techniques in robustness and capacity.

Successfully extracts watermarks from camera-captured images.

Demonstrates practical applicability and robustness of the system.

Abstract

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.