# Splats in Splats: Robust and Effective 3D Steganography towards Gaussian Splatting

arXiv: https://arxiv.org/abs/2412.03121 · HTML: https://mlsanity.com/paper/2412.03121/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [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)
- [Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering](https://mlsanity.com/paper/2312.00109/) (2023)
- [GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting](https://mlsanity.com/paper/2311.14521/) (2023)
- [GS-IR: 3D Gaussian Splatting for Inverse Rendering](https://mlsanity.com/paper/2311.16473/) (2023)
- [CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians](https://mlsanity.com/paper/2404.01133/) (2024)
- [CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization](https://mlsanity.com/paper/2311.18159/) (2023)
- [EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images](https://mlsanity.com/paper/2405.20224/) (2024)
- [DVMark: A Deep Multiscale Framework for Video Watermarking](https://mlsanity.com/paper/2104.12734/) (2021)
- [Large-capacity and Flexible Video Steganography via Invertible Neural Network](https://mlsanity.com/paper/2304.12300/) (2023)
- [GaussianDreamerPro: Text to Manipulable 3D Gaussians with Highly Enhanced Quality](https://mlsanity.com/paper/2406.18462/) (2024)
- [PRTGS: Precomputed Radiance Transfer of Gaussian Splats for Real-Time High-Quality Relighting](https://mlsanity.com/paper/2408.03538/) (2024)
- [SpikeGS: Reconstruct 3D scene via fast-moving bio-inspired sensors](https://mlsanity.com/paper/2407.03771/) (2024)

## Built on by (most cited first, 2 of 2)

- [3DEditSafe: Defending 3D Editing Pipelines from Unsafe Generation](https://mlsanity.com/paper/2605.15398/) (2026)
- [4D-GSW: Kinematic-Aware Spatio-Temporal Consistent Watermarking for 4D Gaussian Splatting](https://mlsanity.com/paper/2605.22342/) (2026)

## Abstract (arXiv)

3D Gaussian splatting (3DGS) has demonstrated impressive 3D reconstruction performance with explicit scene representations. Given the widespread application of 3DGS in 3D reconstruction and generation tasks, there is an urgent need to protect the copyright of 3DGS assets. However, existing copyright protection techniques for 3DGS overlook the usability of 3D assets, posing challenges for practical deployment. Here we describe splats in splats, the first 3DGS steganography framework that embeds 3D content in 3DGS itself without modifying any attributes. To achieve this, we take a deep insight into spherical harmonics (SH) and devise an importance-graded SH coefficient encryption strategy to embed the hidden SH coefficients. Furthermore, we employ a convolutional autoencoder to establish a mapping between the original Gaussian primitives' opacity and the hidden Gaussian primitives' opacity. Extensive experiments indicate that our method significantly outperforms existing 3D steganography techniques, with 5.31% higher scene fidelity and 3x faster rendering speed, while ensuring security, robustness, and user experience.