Robust Video Watermarking Network Based on Channel Spatial Attention

Robust video watermarking refers to the ability to extract the originally embedded watermark information from a video even after malicious modifications and attacks. Currently, traditional watermarking methods have the drawback of lacking robustness against multiple watermark attacks simultaneously. Neural network-based approaches have not fully considered the multi-scale features of videos and tend to lose information during the fusion of scale features. Therefore, we propose a video watermarking scheme based on Channel Spatial Attention. Our model can extract feature information at different scales, allowing the watermark to adapt to features of different scales in the video. Through a series of comparative experiments, our method has shown significant improvements over traditional video watermarking methods and deep learning-based video watermarking models.