Signpost Watermarking: Joint Optimization for Visual Watermark Coexistence
arXiv:2608.10091v1 Announce Type: new Abstract: 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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[Submitted on 10 Aug 2026]
Title:Signpost Watermarking: Joint Optimization for Visual Watermark Coexistence
View a PDF of the paper titled Signpost Watermarking: Joint Optimization for Visual Watermark Coexistence, by Shruti Agarwal and 2 other authors
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Abstract: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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.10091 [cs.CV]
(or arXiv:2608.10091v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.10091
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shruti Agarwal [view email] [v1] Mon, 10 Aug 2026 18:03:02 UTC (9,397 KB)
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