DiffIP: Representation Fingerprints for Robust IP Protection of Diffusion Models
Abstract
Intellectual property (IP) protection for diffusion models is a critical concern, given the significant resources and time required for their development. To effectively safeguard the IP of diffusion models, a key step is enabling the comparison of unique identifiers (fingerprints) between suspect and victim models. However, performing robust and effective fingerprint comparisons among diffusion models remains an under-explored challenge, particularly for diffusion models that have already been released. To address this, in this work, we propose DiffIP, a novel framework for robust and effective fingerprint comparison between suspect and victim diffusion models. Extensive experiments demonstrate the efficacy of our framework.
Cite
Text
Li et al. "DiffIP: Representation Fingerprints for Robust IP Protection of Diffusion Models." International Conference on Computer Vision, 2025.Markdown
[Li et al. "DiffIP: Representation Fingerprints for Robust IP Protection of Diffusion Models." International Conference on Computer Vision, 2025.](https://mlanthology.org/iccv/2025/li2025iccv-diffip/)BibTeX
@inproceedings{li2025iccv-diffip,
title = {{DiffIP: Representation Fingerprints for Robust IP Protection of Diffusion Models}},
author = {Li, Zhuoling and Qu, Haoxuan and Kuen, Jason and Gu, Jiuxiang and Ke, Qiuhong and Liu, Jun and Rahmani, Hossein},
booktitle = {International Conference on Computer Vision},
year = {2025},
pages = {17035-17045},
url = {https://mlanthology.org/iccv/2025/li2025iccv-diffip/}
}