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Where, What, Why: Toward Explainable 3D-GS Watermarking

arXiv cs.CV / 3/11/2026

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Key Points

  • The paper introduces a novel framework for watermarking 3D Gaussian Splatting representations that distinguishes where to embed watermarks from how to maintain visual quality.
  • A Trio-Experts module selects Gaussian primitives for watermark carriers with safety and budget constraints managed by a dedicated gate to balance robustness and bitrate.
  • A channel-wise group mask controls gradient updates to limit artifacts and preserve high-frequency details without impacting runtime, maintaining fidelity.
  • The method supports view-consistent watermark persistence, is robust to common distortions, and outperforms previous techniques with a PSNR improvement of +0.83 dB and a bit-accuracy gain of +1.24%.
  • Decoupled finetuning enables explainability by identifying which Gaussians carry messages and why, facilitating auditability of the watermarking process.

Computer Science > Computer Vision and Pattern Recognition

arXiv:2603.08809 (cs)
[Submitted on 9 Mar 2026]

Title:Where, What, Why: Toward Explainable 3D-GS Watermarking

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Abstract:As 3D Gaussian Splatting becomes the de facto representation for interactive 3D assets, robust yet imperceptible watermarking is critical. We present a representation-native framework that separates where to write from how to preserve quality. A Trio-Experts module operates directly on Gaussian primitives to derive priors for carrier selection, while a Safety and Budget Aware Gate (SBAG) allocates Gaussians to watermark carriers, optimized for bit resilience under perturbation and bitrate budgets, and to visual compensators that are insulated from watermark loss. To maintain fidelity, we introduce a channel-wise group mask that controls gradient propagation for carriers and compensators, thereby limiting Gaussian parameter updates, repairing local artifacts, and preserving high-frequency details without increasing runtime. Our design yields view-consistent watermark persistence and strong robustness against common image distortions such as compression and noise, while achieving a favorable robustness-quality trade-off compared with prior methods. In addition, decoupled finetuning provides per-Gaussian attributions that reveal where the message is carried and why those carriers are selected, enabling auditable explainability. Compared with state-of-the-art methods, our approach achieves a PSNR improvement of +0.83 dB and a bit-accuracy gain of +1.24%.
Comments:
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2603.08809 [cs.CV]
  (or arXiv:2603.08809v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2603.08809
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arXiv-issued DOI via DataCite

Submission history

From: Mingshu Cai [view email]
[v1] Mon, 9 Mar 2026 18:05:27 UTC (32,870 KB)
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