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RAW: Robust Avatar Watermarking -- Benchmarking and Baseline
Jack Parry, · 2026-05-26 · via cs.AI updates on arXiv.org

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Abstract:Digital avatar watermarking presents unique challenges: avatars are routinely post-processed with background replacement, reframing, and format conversion before deployment. We introduce \textbf{RAW} (Robust Avatar Watermarking), a benchmark comprising 50 synthetic avatar videos from 5 commercial providers and 6 attacks simulating real-world avatar workflows. Evaluating 7 existing methods reveals that avatar-specific attacks such as background removal significantly degrade watermark recovery. We propose \textbf{WALT} (Watermarking Avatars with Learned Textures), which embeds watermarks in UV texture space via 3D face reconstruction. WALT achieves the highest robustness to zoom attacks (92.4\%) while maintaining strong performance on background removal (95.6\%). We release our benchmark to facilitate research into avatar-specific watermarking.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.23994 [cs.CV]
  (or arXiv:2605.23994v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2605.23994

arXiv-issued DOI via DataCite (pending registration)

Related DOI: https://doi.org/10.2312/egs.20261006

DOI(s) linking to related resources

Submission history

From: Jack Parry [view email]
[v1] Sun, 17 May 2026 23:01:39 UTC (9,873 KB)