DynMark: A Robust Watermarking Solution for Dynamic Screen Content with Small-size Screenshot Support

Changyu Rao, Gaozhi Liu, Sheng Li, Xinpeng Zhang, Zhenxing Qian

Abstract: Screenshot tools bring convenience to daily workflows but simultaneously pose risks of screen content leakage. Most existing image watermarking methods struggle to protect dynamic screen content (DSC) due to two key limitations: low generalizability across diverse file types and reliance on cover images, which cause difficulties in handling diverse and dynamic screen content. Recently proposed screen-targeted watermarking methods offer cover-independent, fast-response solutions for DSC protection, but they mainly support large-scale screenshots and struggle to balance robustness and visual quality, limiting real-world applicability. To address these issues, in this work, we propose DynMark, a novel cover-independent watermarking scheme for DSC protection. Our method generates a watermark mask that is directly overlaid onto the screen surface for embedding, without relying on the underlying content. As a result, the approach maintains the same watermark mask even when the screen content changes, ensuring stability without the need for updates. Specifically, we use an invertible neural network (INN) to generate watermark and location blocks, jointly optimized with the decoder and locator. Additionally, edge smoothing is applied to further enhance visual quality. These components are integrated into a three-stage training framework to ensure robust performance. This design ensures stable extraction even from small screenshots with size down to 256 x 256, overcoming the limitations of existing methods regarding screenshot size. Extensive experiments show that our method achieves superior visual quality, extraction accuracy, and adaptability to different screenshot tools and screen resolutions, offering an efficient and practical solution for protecting screen content.

External IDs:doi:10.1145/3746027.3754897