Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models

arXiv:2606.12977v1 Announce Type: cross Abstract: Model fingerprinting, embedding user-specific identifiers (fingerprints) into generated outputs, has recently emerged as a popular solution to protect the intellectual property rights (IPR) of generative text-to-image (T2I) models and prevent unauthorized redistribution. In this work, we reveal a previously unexplored systematic vulnerability in existing generative model fingerprinting methods: they lack robustness against collusion attacks, where multiple attackers combine their models to remove or obscure the fingerprints. To address this iss

Source: arXiv cs.AI — read the full report at the original publisher.

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