Fake3DGS

This is the official PyTorch implementation of the paper "Fake3DGS: A Benchmark for 3D Manipulation Detection in Neural Rendering" (ICPR 2026).

1. Setup

Environment Setup

You can find all the packages and dependencies in the environment.yml file. If you have conda, you can simply run

conda env create -f environment.yml

2. Download the dataset

Download the dataset HERE There are two versions of the dataset:

• The first one contains nerfstudio checkpoints compressed.
• The second one contains each gaussian splats extracted features inside a pkl file.

We used the second one in model training. Make sure to extract gaussian_pickles.tar.gz inside this folder. You can also download the first version of the dataset for further experiments.

3. Training

python -u train.py -bs 4 -e 8 --config mixed_training.pkl --name new_mixed --resume new_mixed_5.pth

Explanation of Parameters:

• --bs : Batch size.
• -e : Number of epochs.
• --config: config pkl file.
• --name: Name of the training.
• --resume: Checkpoint from which to resume training.

4. Testing

To test the model, download the corresponding pretrained weights HERE and place them in the folder:

checkpoints/

Then run:

python -u test.py -bs 4 --test_set "mixed_training.pkl" --ckpt "checkpoints/best.pth"

Explanation of Parameters:

• --bs : Batch size.
• --ckpt : Model weights to load.
• --test_set: config pkl file.

Citation

If you find our work useful for your project, please consider citing the paper:

@inproceedings{ddinuccifake3dgs,
  title={Fake3DGS: A Benchmark for 3D Manipulation Detection in Neural Rendering},
  author={Di Nucci, Davide and Catalini, Riccardo and Borghi, Guido and Vezzani, Roberto},
  booktitle={Twentyeighth International Conference on Pattern Recognition},
  year=2026
}

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