ConcealGS: Conceal Implicit Information in 3D Gaussian Splatting

Yifeng Yang*, Hengyu Liu*, Chenxin Li†, Yining Sun, Wuyang Li, Yifan Liu, Yiyang Lin, Yixuan Yuan, Nanyang Ye (* indicates equal contribution, † indicates the project lead)
| Full Paper | code

Abstract: With the rapid development of 3D reconstruction technology, the widespread distribution of 3D data has become a future trend. While traditional visual data (such as images and videos) and NeRF-based formats already have mature techniques for copyright protection, steganographic techniques for the emerging 3D Gaussian Splatting (3D-GS) format have yet to be fully explored. To address this, we propose ConcealGS, an innovative method for embedding implicit information into 3D-GS. By introducing the knowledge distillation and gradient optimization strategy based on 3D-GS, ConcealGS overcomes the limitations of NeRF-based models and enhances the robustness of implicit information and the quality of 3D reconstruction. We evaluate ConcealGS in various potential application scenarios, and experimental results have demonstrated that ConcealGS not only successfully recovers implicit information but also has almost no impact on rendering quality, providing a new approach for embedding invisible and recoverable information into 3D models in the future.

BibTeX

@article{yang2025concealgs,
  title={ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting},
  author={Yang, Yifeng and Liu, Hengyu and Li, Chenxin and Sun, Yining and Li, Wuyang and Liu, Yifan and Lin, Yiyang and Yuan, Yixuan and Ye, Nanyang},
  journal={arXiv preprint arXiv:2501.03605},
  year={2025}
}

Setup

Environment

Our default, provided install method is based on Conda package and environment management:

git clone https://github.com/zxk1212/ConcealGS.git
git submodule update --init --recursive

conda create --name conceal-gs python=3.8
conda activate conceal-gs

PyTorch (Please check your CUDA version, we used 11.8)

pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu118

Required packages

pip install -r requirements.txt

Submodules

pip install -e submodules/diff-gaussian-rasterization
pip install -e submodules/simple-knn

Pretrain

Get a set of well-trained Gaussians with the training process like 3D Gaussian Splatting

python train.py \
  -s $DATA_PATH \
  --port 6016 \
  --model_path $MODEL_PATH1 \
  --iterations 10000 \
  --resolution 4 \
  --white_background \
  --detector_lr 0.0001 \
  --eval

python render.py -m $MODEL_PATH1
python metrics.py -m $MODEL_PATH1

ConcealGS

Complete running scripts is in train_scripts/run.sh

Train

python train_stega.py \
  -s $DATA_PATH \
  --start_checkpoint ${MODEL_PATH1}/chkpnt10000.pth \
  --watermark_path /path/to/wm/${wm_item}.png \
  --model_path $MODEL_PATH2 \
  --port 6018 \
  --resolution 4 \
  --iterations 20000 \
  --target_weight 1.0 \
  --white_background \
  --detector_lr 0.0001 \
  --eval

Render and metric

python render_stega.py \
  -m $MODEL_PATH2 \
  --watermark_path /path/to/wm/${wm_item}.png \
  --iteration 20000 \
  --skip_train

python metrics.py \
  -m $MODEL_PATH2

Acknowledgement

Our repo is developed based on 3D Gaussian Splatting, StegaNeRF.