多种水印可共存且互不干扰，还能组合提升容量与鲁棒性。
On the Coexistence and Ensembling of Watermarks
- 首次研究深度图像水印共存问题，发现不同开源水印可兼容共存。
- 水印共存对图像质量与解码鲁棒性影响极小，均在容忍范围内。
- 无需重训练即可通过集成提升信息容量与性能权衡灵活性。
水印技术通过在图像、视频、音频和文本等媒体中嵌入不可感知的信息，实现知识产权保护、内容溯源与归属追踪。随着数字生态日益复杂，同一媒体需承载多种用途的水印。为检测与解码所有水印，它们必须良好共存。本文首次系统研究深度图像水印方法的共存性，出人意料地发现，多种开源水印可在保持图像质量与解码鲁棒性基本不受影响的前提下共存。水印共存为集成多种水印方法开辟了新路径。我们展示了如何通过集成提升整体消息容量，并在不重新训练基础模型的情况下，实现容量、准确率、鲁棒性与图像质量间的新型权衡。
原文摘要 · Abstract (English)
Watermarking, the practice of embedding imperceptible information into media such as images, videos, audio, and text, is essential for intellectual property protection, content provenance and attribution. The growing complexity of digital ecosystems necessitates watermarks for different uses to be embedded in the same media. However, to detect and decode all watermarks, they need to coexist well with one another. We perform the first study of coexistence of deep image watermarking methods and, contrary to intuition, we find that various open-source watermarks can coexist with only minor impacts on image quality and decoding robustness. The coexistence of watermarks also opens the avenue for ensembling watermarking methods. We show how ensembling can increase the overall message capacity and enable new trade-offs between capacity, accuracy, robustness and image quality, without needing to retrain the base models.
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