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SHield watermarks 4D Gaussian Splatting files

Original title: SHield: Watermarking 4D Gaussian Splatting via Spherical Harmonic Coefficient Perturbation

arXiv cs.CRby Kunal Bhosikar ()1 min read
AI summary · x-ai/grok-4.7Summaries by Vani AI

SHield embeds payloads in 4DGS via SH coefficient perturbation for decoder-free ownership verification.

4D Gaussian Splatting models are distributed as parameter files vulnerable to copying. SHield tiles zero-mean payloads across spherical harmonic coefficients of selected Gaussians. Geometric re-association using canonical centers survives tampering. Experiments on 30 scenes confirm near-perfect recovery with <=0.13 dB rendering penalty.

Key facts
  • Payload recovery with Pearson r >= 0.991 across 150 models
  • Survives INT8 quantization and 30% opacity pruning
  • Geometric re-association for tamper resistance
  • Held-out evaluation shows zero false positives
  • Applies brief anchored optimization for fidelity
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From the original article

1 min read at arxiv.org

4D Gaussian Splatting (4DGS) models are computationally expensive to train but distributed as easily copied parameter files. Existing watermarks protect these assets by fine-tuning the scene against a neural decoder to extract bit strings from rendered video, a heavy process vulnerable to the temporal dynamics of dynamic scenes.

arXiv cs.CR

By Kunal Bhosikar · arxiv.org

Read the full article at arXiv cs.CR

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.