DiffIP: Representation Fingerprints for Robust IP Protection of Diffusion Models

Zhuoling Li, Haoxuan Qu, Jason Kuen, Jiuxiang Gu, Qiuhong Ke, Jun Liu, Hossein Rahmani; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 17035-17045

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.