Robust Image Watermarking Based on Generative Adversarial Networks for Copyright Protection

Abstract Digital media is easy to be copied, which leads to a proliferation of copyright infringement. One proposed solution is digital watermarking technology, which is to embed message bits into multimedia carriers such as images and videos to prove the creator’s ownership of his work. Most recently, with the upsurge of convolutional neural network in the terms of artificial intelligence, deep learning has made great achievements in digital watermarking. In this work, we propose a new framework with robust image watermarking based on a generative adversarial network (RIW-GAN). With proposed method, the encoder network is composed of convolutional layers and a residual block outputs the encoded image that has low distortion and is closer to the original image. To enhance the robustness to attack of the model, a simulated noise layer as a differentiable network layer is applied to promote end-to-end training before decoding. Therefore, the proposed model has higher accuracy rate for decoding the attacked encoded image. In comparison to the state-of-the-art model, the experimental results demonstrate that RIW-GAN has superior invisibility and stronger robustness against regular attacks like JPEG compression and geometric attacks such as resizing and cropping.