Citation

If you use MarkDiffusion in your research, please cite our paper:

BibTeX

@article{pan2025markdiffusion,
  title={MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models},
  author={Pan, Leyi and Guan, Sheng and Fu, Zheyu and Si, Luyang and Wang, Zian and Hu, Xuming and King, Irwin and Yu, Philip S and Liu, Aiwei and Wen, Lijie},
  journal={arXiv preprint arXiv:2509.10569},
  year={2025}
}

APA Style

Pan, L., Guan, S., Fu, Z., Si, L., Wang, Z., Hu, X., King, I., Yu, P. S., Liu, A., & Wen, L. (2025). MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models. arXiv preprint arXiv:2509.10569.

MLA Style

Pan, Leyi, et al. “MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models.” arXiv preprint arXiv:2509.10569 (2025).

Algorithm-Specific Citations

If you use specific algorithms, please also cite their original papers:

Tree-Ring Watermark

@misc{wen2023treeringwatermarksfingerprintsdiffusion,
   title={Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust},
   author={Yuxin Wen and John Kirchenbauer and Jonas Geiping and Tom Goldstein},
   year={2023},
   eprint={2305.20030},
   archivePrefix={arXiv},
   primaryClass={cs.LG},
   url={https://arxiv.org/abs/2305.20030},
}

Ring-ID

@article{ci2024ringid,
   title={RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-Key Identification},
   author={Ci, Hai and Yang, Pei and Song, Yiren and Shou, Mike Zheng},
   journal={arXiv preprint arXiv:2404.14055},
   year={2024}
}

ROBIN

@inproceedings{huangrobin,
   title={ROBIN: Robust and Invisible Watermarks for Diffusion Models with Adversarial Optimization},
   author={Huang, Huayang and Wu, Yu and Wang, Qian},
   booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems}
}

WIND

@article{arabi2024hidden,
   title={Hidden in the Noise: Two-Stage Robust Watermarking for Images},
   author={Arabi, Kasra and Feuer, Benjamin and Witter, R Teal and Hegde, Chinmay and Cohen, Niv},
   journal={arXiv preprint arXiv:2412.04653},
   year={2024}
}

SFW

@inproceedings{lee2025semantic,
   title={Semantic Watermarking Reinvented: Enhancing Robustness and Generation Quality with Fourier Integrity},
   author={Lee, Sung Ju and Cho, Nam Ik},
   booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
   pages={18759--18769},
   year={2025}
}

Gaussian-Shading

@article{yang2024gaussian,
   title={Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models},
   author={Yang, Zijin and Zeng, Kai and Chen, Kejiang and Fang, Han and Zhang, Weiming and Yu, Nenghai},
   journal={arXiv preprint arXiv:2404.04956},
   year={2024},
}

GaussMarker

@misc{li2025gaussmarkerrobustdualdomainwatermark,
   title={GaussMarker: Robust Dual-Domain Watermark for Diffusion Models},
   author={Kecen Li and Zhicong Huang and Xinwen Hou and Cheng Hong},
   year={2025},
   eprint={2506.11444},
   archivePrefix={arXiv},
   primaryClass={cs.CR},
   url={https://arxiv.org/abs/2506.11444},
}

PRC

@article{gunn2025undetectable,
   title={An undetectable watermark for generative image models},
   author={Gunn, Sam and Zhao, Xuandong and Song, Dawn},
   journal={arXiv preprint arXiv:2410.07369},
   year={2024}
}

SEAL

@article{arabi2025seal,
   title={SEAL: Semantic Aware Image Watermarking},
   author={Arabi, Kasra and Witter, R Teal and Hegde, Chinmay and Cohen, Niv},
   journal={arXiv preprint arXiv:2503.12172},
   year={2025}
}

VideoShield

@inproceedings{hu2025videoshield,
   title={VideoShield: Regulating Diffusion-based Video Generation Models via Watermarking},
   author={Runyi Hu and Jie Zhang and Yiming Li and Jiwei Li and Qing Guo and Han Qiu and Tianwei Zhang},
   booktitle={International Conference on Learning Representations (ICLR)},
   year={2025}
}

VideoMark

@article{hu2025videomark,
   title={VideoMark: A Distortion-Free Robust Watermarking Framework for Video Diffusion Models},
   author={Hu, Xuming and Li, Hanqian and Li, Jungang and Liu, Aiwei},
   journal={arXiv preprint arXiv:2504.16359},
   year={2025}
}

Acknowledgments

We would like to thank:

  • All contributors to the MarkDiffusion project

  • The authors of the watermarking algorithms implemented in this toolkit

  • The open-source community for their valuable feedback and contributions

  • Research institutions supporting this work

Using MarkDiffusion in Publications

When using MarkDiffusion in your research:

  1. Cite the main MarkDiffusion paper (required)

  2. Cite specific algorithm papers you use (required)

  3. Mention the toolkit in your acknowledgments

  4. Link to the GitHub repository

Example acknowledgment text:

“This research utilized MarkDiffusion [1], an open-source toolkit for generative watermarking. We specifically employed the Gaussian-Shading algorithm [2] for watermark embedding and detection.”

License

MarkDiffusion is released under the MIT License. See the LICENSE file for details.

When using MarkDiffusion, please ensure compliance with the licenses of:

  • Individual watermarking algorithms

  • Pre-trained models

  • Datasets used for evaluation

Contact

For questions about citation or collaboration:

Updates

This citation information was last updated: November 2025

For the most up-to-date citation information, please check:

  • The project README

  • The paper on arXiv

  • The project homepage