Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
GS-Hider: Hiding Messages into 3D Gaussian Splatting
Authors: Xuanyu Zhang, Jiarui Meng, Runyi Li, Zhipei Xu, yongbing zhang, Jian Zhang
NeurIPS 2024 | Venue PDF | 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. |