# GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting

arXiv: https://arxiv.org/abs/2411.19895 · HTML: https://mlsanity.com/paper/2411.19895/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 56 of its 77 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 30 of 56)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields](https://mlsanity.com/paper/2011.12100/) (2020)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Mip-Splatting: Alias-free 3D Gaussian Splatting](https://mlsanity.com/paper/2311.16493/) (2023)
- [SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering](https://mlsanity.com/paper/2311.12775/) (2023)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [Gaussian Splatting SLAM](https://mlsanity.com/paper/2312.06741/) (2023)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Splatter Image: Ultra-Fast Single-View 3D Reconstruction](https://mlsanity.com/paper/2312.13150/) (2023)
- [Neural RGB-D Surface Reconstruction](https://mlsanity.com/paper/2104.04532/) (2021)
- [The Stable Signature: Rooting Watermarks in Latent Diffusion Models](https://mlsanity.com/paper/2303.15435/) (2023)
- [LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching](https://mlsanity.com/paper/2311.11284/) (2023)

## Built on by (most cited first, 18 of 18)

- [AdLift: Lifting Adversarial Perturbations to Safeguard 3D Gaussian Splatting Assets Against Instruction-Driven Editing](https://mlsanity.com/paper/2512.07247/) (2025)
- [SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation](https://mlsanity.com/paper/2507.05256/) (2025)
- [Intellectual Property Protection for 3D Gaussian Splatting Assets: A Survey](https://mlsanity.com/paper/2602.03878/) (2026)
- [X-SG2S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks](https://mlsanity.com/paper/2502.10475/) (2025)
- [RDSplat: Robust Watermarking for 3D Gaussian Splatting Against 2D and 3D Diffusion Editing](https://mlsanity.com/paper/2512.06774/) (2025)
- [Fake3DGS: A Benchmark for 3D Manipulation Detection in Neural Rendering](https://mlsanity.com/paper/2604.27590/) (2026)
- [Rotation-Invariant Spherical Watermarking via Third-Order SO(3) Representation Coupling](https://mlsanity.com/paper/2605.26702/) (2026)
- [Position: 3D Gaussian Splatting Watermarking Should Be Scenario-Driven and Threat-Model Explicit](https://mlsanity.com/paper/2602.02602/) (2026)
- [StegoNGP: 3D Cryptographic Steganography using Instant-NGP](https://mlsanity.com/paper/2603.00949/) (2026)
- [PatchPoison: Poisoning Multi-View Datasets to Degrade 3D Reconstruction](https://mlsanity.com/paper/2604.13153/) (2026)
- [Splats in Splats++: Robust and Generalizable 3D Gaussian Splatting Steganography](https://mlsanity.com/paper/2604.15862/) (2026)
- [GuardMarkGS: Unified Ownership Tracing and Edit Deterrence for 3D Gaussian Splatting](https://mlsanity.com/paper/2605.12919/) (2026)
- [4D-GSW: Kinematic-Aware Spatio-Temporal Consistent Watermarking for 4D Gaussian Splatting](https://mlsanity.com/paper/2605.22342/) (2026)
- [Characterizing Detectability in 3DGS Poisoning: A Stage-wise Benchmark](https://mlsanity.com/paper/2606.03499/) (2026)
- [IBRSteG: Learning a Generalizable Steganography Framework for 3D Gaussian Splatting](https://mlsanity.com/paper/2606.30024/) (2026)
- [EvTrajGS: Accurate and Efficient 3D Gaussian Splatting from Unposed Event Streams](https://mlsanity.com/paper/2608.08585/) (2026)
- [NGS-Marker: Robust Native Watermarking for 3D Gaussian Splatting](https://mlsanity.com/paper/2608.17447/) (2026)
- [MoSign: Challenge-Response Motion-Watermark Authentication for Anonymous Virtual-Reality Users](https://mlsanity.com/paper/2609.29603/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) has recently created impressive 3D assets for various applications. However, considering security, capacity, invisibility, and training efficiency, the copyright of 3DGS assets is not well protected as existing watermarking methods are unsuited for its rendering pipeline. In this paper, we propose GuardSplat, an innovative and efficient framework for watermarking 3DGS assets. Specifically, 1) We propose a CLIP-guided pipeline for optimizing the message decoder with minimal costs. The key objective is to achieve high-accuracy extraction by leveraging CLIP's aligning capability and rich representations, demonstrating exceptional capacity and efficiency. 2) We tailor a Spherical-Harmonic-aware (SH-aware) Message Embedding module for 3DGS, seamlessly embedding messages into the SH features of each 3D Gaussian while preserving the original 3D structure. This enables watermarking 3DGS assets with minimal fidelity trade-offs and prevents malicious users from removing the watermarks from the model files, meeting the demands for invisibility and security. 3) We present an Anti-distortion Message Extraction module to improve robustness against various distortions. Experiments demonstrate that GuardSplat outperforms state-of-the-art and achieves fast optimization speed. Project page is at https://narcissusex.github.io/GuardSplat, and Code is at https://github.com/NarcissusEx/GuardSplat.