End-to-end robust and transparent image watermarking method for screen-shooting scenarios

Nana Yang, Yi Chen, Hongxia Wang, Guowei Shen, Chun Guo, Yunhe Cui, Yong Liu, Hanzhou Wu

Abstract: Screen-shooting robust image watermarking techniques are the primary means for copyright protection and leakage traceability of screen content. However, existing image watermarking methods generate watermarked images with visible artifacts and are not robust enough against attacks. Therefore, we propose an end-to-end robust and transparent image watermarking method for protecting copyright in screen-shooting scenarios. In this method, we introduce scaling distortion to combine with color distortion, moiré distortion, Gaussian noise, and JPEG compression to design a new noise layer. In the noise layer, we propose two ways to simulate real moiré distortion. We also propose a training strategy adding five distortions in stages during end-to-end adversarial training. Both the noise layer and training strategy can help improve robustness. In addition, we propose an edge-aware loss function to guide the encoder to focus on modifying the edge regions of the cover image, thereby enhancing transparency. The experimental results show that, under different shooting conditions, the watermark extraction accuracy exceeds 97%, indicating the high robustness of our proposed method. In terms of transparency, the peak signal-to-noise ratio reaches 38.51 dB, which is 1.34 dB higher than the state-of-the-art deep learning–based screen-shooting watermarking method, PIMoG.