让多个不可见水印共存，提升内容溯源可靠性。
Signpost Watermarking: Joint Optimization for Visual Watermark Coexistence
- 联合优化水印编码与解码，增强多水印共存能力
- 视频水印同样可实现低干扰共存，且解码鲁棒性提升
- 适合内容版权追踪与多层可信溯源场景
我们提出一种训练不可见视觉水印的方法，使其能与其他水印共存。近期研究表明，独立训练的图像水印模型可在极少干扰下共存，支持水印集成。但这种共存是偶然现象，并非显式优化目标，可能导致解码鲁棒性或视觉质量下降。我们首先实证发现该共存特性同样适用于视频水印。随后，我们通过引入解码感知目标，对图像与视频水印进行联合优化，显著改善共存性能。结果表明，该方法为部署‘路标水印’提供了可行路径，可标识独立部署的来源水印系统，支持内容真实性和版权信息的分层信号传递。
原文摘要 · Abstract (English)
We present a method for training imperceptible visual watermarks to coexist with other such watermarks. Recent work has shown that independently trained image watermarking models can coexist with surprisingly limited interference, enabling watermark ensembling. However, this coexistence is a serendipitous property rather than an explicit optimization objective, leaving interference uncontrolled and potentially reducing decoding robustness or visual quality. We first show empirically that the same coexistence property extends to video watermarking. We then show that both image and video watermarks can be trained with a decoder-aware objective to improve coexistence. Our results suggest a practical path to signpost watermarks that indicate the presence of independently deployed provenance watermarking systems, supporting layered provenance signaling for content authenticity and rights.
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