Robust and Compatible Video Watermarking via Spatio-Temporal Enhancement and Multiscale Pyramid Attention

Deep learning-based video watermarking is shown to be effective in improving robustness. However, existing methods neglect the enhancement of long-distance spatio-temporal features and the representation of the inter-frame difference and the intra-frame difference, which lead to poor robustness against H.264 compression and low compatibility with high-definition (HD) and full high-definition (FHD) videos for copyright protection, respectively. To address these issues, we propose a robust and compatible video watermarking network (RC-VWN) based on spatio-temporal enhancement and multiscale pyramid attention. For robustness, RC-VWN extracts long-distance spatio-temporal features using a central difference 3D U-Net and enhances them through multiscale spatio-temporal fusion, which alleviates the loss of the watermark caused by attacks through the association of long-distance spatio-temporal features. Then, the simulated compression network is developed to simulate H.264 compression with high-accuracy, which guides the decoder to recover the watermark accurately. For compatibility, a multiscale pyramid attention is designed to represent the intra-frame difference and the inter-frame difference effectively. Experimental results demonstrate that RC-VWN outperforms the state-of-the-art methods with higher robustness and imperceptibility under quantitative evaluation and visual quality. Furthermore, RC-VWN exhibits high compatibility with various videos, including HD and FHD videos, ensuring effective copyright protection.