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arxiv: 2511.22262 · v2 · submitted 2025-11-27 · 💻 cs.CV

Recognition: 1 theorem link

· Lean Theorem

Can Protective Watermarking Safeguard the Copyright of 3D Gaussian Splatting?

Wenkai Huang , Yijia Guo , Gaolei Li , Lei Ma , Hang Zhang , Liwen Hu , Jiazheng Wang , Jianhua Li
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Tiejun Huang
Authors on Pith no claims yet

Pith reviewed 2026-05-17 05:04 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D Gaussian SplattingWatermarkingCopyright ProtectionWatermark RemovalGaussian Primitives3D Scene RepresentationPurification Attack
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The pith

Watermarks embedded in 3D Gaussian Splatting scenes can be isolated and stripped out by targeting specific Gaussian primitives.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines whether specialized watermarking truly protects the copyright of 3D Gaussian Splatting assets. It finds that ordinary 2D removal methods fail because of the unique per-primitive rendering and view dependence in 3DGS. The authors therefore introduce GSPure, which examines each Gaussian's contribution across views and groups them by geometric features to identify and delete only the watermark-bearing ones. Experiments show this step reduces watermark strength by as much as 16.34 dB while keeping the rendered scene within 1 dB of its original quality. A sympathetic reader would conclude that current 3DGS watermarking offers only fragile ownership signals rather than reliable protection.

Core claim

Existing 3D Gaussian Splatting watermarking schemes are vulnerable because watermark-related Gaussian primitives can be isolated by analyzing their view-dependent rendering contributions and by performing geometrically accurate feature clustering; once these primitives are removed, the watermark signal drops sharply while scene fidelity is preserved.

What carries the argument

GSPure, a purification pipeline that scores each Gaussian by its view-dependent rendering contribution and then clusters them by accurate geometric features to isolate and excise watermark primitives.

If this is right

  • Standard 2D image watermark removal techniques do not transfer effectively to 3DGS because of its specialized rendering pipeline.
  • Watermark Gaussians can be excised while the remaining scene representation stays visually intact.
  • GSPure outperforms prior purification methods on both effectiveness and generalization across different watermarking schemes.
  • Ownership verification for 3DGS assets becomes unreliable once such a targeted removal step is available.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • 3D asset owners may need watermark signals that are entangled with the core geometry rather than carried by separable primitives.
  • Future watermark designs for 3DGS should be tested against clustering attacks that exploit view dependence.
  • The same isolation approach could be tried on other explicit 3D representations that store per-point attributes.
  • Legal claims based on 3DGS watermarks may require additional evidence that the mark survives realistic purification attempts.

Load-bearing premise

Watermark-related Gaussian primitives can be precisely isolated and removed using view-dependent rendering contributions and geometrically accurate feature clustering without materially affecting scene integrity or introducing visible artifacts.

What would settle it

Apply GSPure to a set of watermarked 3DGS scenes and measure whether watermark PSNR drops by at least 10 dB on average while rendered image PSNR stays within 1 dB of the unpurified version; if either metric fails consistently, the central claim is false.

Figures

Figures reproduced from arXiv: 2511.22262 by Gaolei Li, Hang Zhang, Jianhua Li, Jiazheng Wang, Lei Ma, Liwen Hu, Tiejun Huang, Wenkai Huang, Yijia Guo.

Figure 1
Figure 1. Figure 1: Visualization of purification results by GSPure on existing scene-level watermarking techniques (Guo et al. 2024c; Zhang et al. 2024). From left to right: watermark extracted by the copyright owner from the original published point cloud; the original published point cloud containing watermark information; feature clustering results which highlights watermark-correlated Gaussian primitives achieved by our … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our GSPure framework. First, we compute the rendering contribution of each Gaussian primitive from multiple viewpoints. Next, we construct geometry-aware features and use HDBSCAN to identify watermark-correlated clusters. Finally, adaptive pruning based on cluster-level and noise-level thresholds (τc and τn) effectively removes watermark￾related Gaussians while preserving original scene fidelit… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparisons on Mipnerf360 datasets. The first row of each group represents the [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of Point Cloud. The left column rep [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for 3D scenes, widely adopted due to its exceptional efficiency and high-fidelity visual quality. Given the significant value of 3DGS assets, recent works have introduced specialized watermarking schemes to ensure copyright protection and ownership verification. However, can existing 3D Gaussian watermarking approaches genuinely guarantee robust protection of the 3D assets? In this paper, for the first time, we systematically explore and validate possible vulnerabilities of 3DGS watermarking frameworks. We demonstrate that conventional watermark removal techniques designed for 2D images do not effectively generalize to the 3DGS scenario due to the specialized rendering pipeline and unique attributes of each gaussian primitives. Motivated by this insight, we propose GSPure, the first watermark purification framework specifically for 3DGS watermarking representations. By analyzing view-dependent rendering contributions and exploiting geometrically accurate feature clustering, GSPure precisely isolates and effectively removes watermark-related Gaussian primitives while preserving scene integrity. Extensive experiments demonstrate that our GSPure achieves the best watermark purification performance, reducing watermark PSNR by up to 16.34dB while minimizing degradation to original scene fidelity with less than 1dB PSNR loss. Moreover, it consistently outperforms existing methods in both effectiveness and generalization. Our code is available at https://github.com/insightlab-CG-3DV/GSPure.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper examines vulnerabilities in existing 3D Gaussian Splatting (3DGS) watermarking schemes for copyright protection. It shows that standard 2D image watermark removal techniques fail to generalize to 3DGS due to its rendering pipeline and per-Gaussian attributes. The authors propose GSPure, which isolates watermark-related Gaussians via analysis of view-dependent rendering contributions combined with geometrically accurate feature clustering, then removes them while preserving scene content. Experiments report that GSPure reduces watermark PSNR by up to 16.34 dB with under 1 dB degradation to scene PSNR and outperforms prior purification methods in effectiveness and generalization.

Significance. If the central results hold under broader conditions, the work is significant for highlighting practical weaknesses in current 3DGS copyright mechanisms and for supplying a concrete purification baseline that could steer development of more robust watermarking. The public code release aids reproducibility and follow-on research.

major comments (1)
  1. [Method and Experiments] The headline performance (up to 16.34 dB watermark-PSNR reduction with <1 dB scene-PSNR loss) rests on the assumption that watermark Gaussians are separable from scene content by view-dependent contribution analysis and feature clustering. If a watermarking scheme instead embeds the mark through small, scene-consistent perturbations to the attributes (color, opacity, SH coefficients, scale) of existing primitives rather than adding distinct Gaussians, the clustering step lacks a reliable signal. The manuscript should explicitly state which watermarking schemes were tested and include results against integrated-embedding attacks to substantiate the generalization claim.
minor comments (2)
  1. [Experiments] Dataset descriptions, exact clustering hyperparameters, and ablation studies on the contribution of view-dependent vs. geometric features are needed for full reproducibility.
  2. [Experiments] Clarify whether the reported PSNR numbers are averaged over multiple scenes or views and include standard deviations.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive and insightful feedback. The major comment correctly identifies a key assumption in our evaluation, and we address it directly below while clarifying the scope of our contributions.

read point-by-point responses

  1. Referee: [Method and Experiments] The headline performance (up to 16.34 dB watermark-PSNR reduction with <1 dB scene-PSNR loss) rests on the assumption that watermark Gaussians are separable from scene content by view-dependent contribution analysis and feature clustering. If a watermarking scheme instead embeds the mark through small, scene-consistent perturbations to the attributes (color, opacity, SH coefficients, scale) of existing primitives rather than adding distinct Gaussians, the clustering step lacks a reliable signal. The manuscript should explicitly state which watermarking schemes were tested and include results against integrated-embedding attacks to substantiate the generalization claim.

    Authors: We thank the referee for this observation. Our method and experiments target existing 3DGS watermarking schemes, which predominantly introduce additional watermark Gaussians as distinct primitives to enable robust ownership verification while preserving scene fidelity; this is the paradigm used by the methods evaluated in our work. In the revised manuscript we will explicitly enumerate the specific watermarking schemes tested, along with their core design choices. Regarding integrated-embedding attacks that apply subtle perturbations directly to existing Gaussian attributes, we acknowledge that the feature clustering component would receive a weaker signal and that our current results do not cover this case. We will add a dedicated discussion subsection clarifying the scope of our evaluation, noting that additive watermarking remains the dominant approach in the literature because it affords better control and lower visual degradation. While we do not introduce new experimental results on integrated embeddings (which would require implementing and benchmarking novel watermarking baselines outside the present study), the explicit listing of tested schemes and the added discussion will strengthen the generalization claim within the context of currently published methods. revision: partial

Circularity Check

0 steps flagged

No significant circularity; GSPure derives from standard 3DGS pipeline analysis and clustering

full rationale

The paper proposes GSPure by analyzing view-dependent rendering contributions from the existing 3DGS pipeline and applying geometrically accurate feature clustering to isolate watermark primitives. These steps rely on the documented 3DGS rendering mechanics and standard clustering algorithms rather than any parameter fitted to the target result or self-referential definition of success. Performance claims (e.g., 16.34 dB watermark PSNR reduction with <1 dB scene loss) are presented as outcomes of experiments on external watermarking schemes, not derived by construction from the method's own inputs. No load-bearing step reduces to a self-citation chain, uniqueness theorem from the authors, or renaming of a known result. The derivation remains self-contained against external benchmarks of 3DGS and clustering.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The approach uses the standard 3DGS rendering model and off-the-shelf feature clustering without introducing new free parameters, axioms, or invented entities beyond the described algorithm.

pith-pipeline@v0.9.0 · 5587 in / 1129 out tokens · 32021 ms · 2026-05-17T05:04:40.958057+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Splats in Splats++: Robust and Generalizable 3D Gaussian Splatting Steganography

    cs.CV 2026-04 conditional novelty 7.0

    Splats in Splats++ embeds messages into 3DGS via importance-graded SH encryption, hash-grid opacity mapping, and a gradient-gated consistency loss, achieving higher fidelity and robustness than prior methods.

Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages · cited by 1 Pith paper

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    Li, D.; Huang, S.-S.; Lu, Z.; Duan, X.; and Huang, H

    3d gaussian splatting for real-time radiance field ren- dering.ACM Transactions on Graphics, 42(4): 1–14. Li, D.; Huang, S.-S.; Lu, Z.; Duan, X.; and Huang, H

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    In ACM SIGGRAPH 2024 Conference Papers, 1–11

    ST-4DGS: Spatial-Temporally Consistent 4D Gaus- sian Splatting for Efficient Dynamic Scene Rendering. In ACM SIGGRAPH 2024 Conference Papers, 1–11. Li, Y .; Lyu, X.; Koren, N.; Lyu, L.; Li, B.; and Ma, X. 2021. Neural attention distillation: Erasing backdoor triggers from deep neural networks.arXiv preprint arXiv:2101.05930. Liang, Z.; Zhang, Q.; Feng, Y ...

    work page arXiv 2024

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    InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 20675–20685

    3dgstream: On-the-fly training of 3d gaussians for efficient streaming of photo-realistic free-viewpoint videos. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 20675–20685. Tu, X.; Radl, L.; Steiner, M.; Steinberger, M.; Kerbl, B.; and de la Torre, F. 2025. VRSplat: Fast and Robust Gaussian Splatting for Virtual Reali...

    work page arXiv 2025

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    Zhang, X.; Meng, J.; Xu, Z.; Yang, S.; Wu, Y .; Wang, R.; and Zhang, J

    GS-Hider: Hiding Messages into 3D Gaussian Splat- ting.arXiv preprint arXiv:2405.15118. Zhang, X.; Meng, J.; Xu, Z.; Yang, S.; Wu, Y .; Wang, R.; and Zhang, J. 2025. Securegs: Boosting the security and fi- delity of 3d gaussian splatting steganography.arXiv preprint arXiv:2503.06118

    work page arXiv 2025