GS-Hider: Hiding Messages into 3D Gaussian Splatting
Authors: Xuanyu Zhang, Jiarui Meng, Runyi Li, Zhipei Xu, yongbing zhang, Jian Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrated that the proposed GS-Hider can effectively conceal multimodal messages without compromising rendering quality and possesses exceptional security, robustness, capacity, and flexibility. Our project is available at: https://xuanyuzhang21. github.io/project/gshider/. |
| Researcher Affiliation | Academia | School of Electronic and Computer Engineering, Peking University 2 Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Peking University Shenzhen Graduate School 3 School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) |
| Pseudocode | No | The paper describes its methods in text and diagrams but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our project is available at: https://xuanyuzhang21. github.io/project/gshider/. This link leads to a project page, not directly to a source-code repository as strictly required. |
| Open Datasets | Yes | We conduct experiments on 9 original scenes taken from the public Mip-Ne RF360 dataset [2]. |
| Dataset Splits | No | The paper mentions 'training views' but does not explicitly provide percentages, sample counts, or citations to predefined validation splits for the dataset used in its experiments. |
| Hardware Specification | Yes | We conduct all our experiments on a NVIDIA RTX 4090 Server. |
| Software Dependencies | No | The paper mentions 'CUDA rasterizer' but does not provide specific version numbers for it or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | λ is set to 0.5 when hiding 3D scenes and set to 0.1 when hiding a single image. β and γ in Eq. 6 and Eq. 7 are respectively set to 0.2. The feature dimension M is set to 16. |