Fingerprinting Text-to-Image Diffusion Models via Collapsed Generation
Proprietary text-to-image diffusion models are increasingly distributed as hosted services and downloadable checkpoints, making their intellectual property (IP) protection an increasingly critical concern when model leakage, copying, or unauthorized fine-tuning is disputed. In this work, we present a non-invasive model fingerprinting framework based on \emph{collapsed generation}, a phenomenon where certain input conditions produce highly consistent images across multiple stochastic seeds. We show that collapsed generation is an intrinsic, model-dependent property of the learned generation process. These collapse-prone conditions therefore expose model-specific behavioral signatures, enab...
| Authors | Yuanmin Huang, Chen Chen, Geng Hong et al. |
|---|---|
| Primary category | cs.CR |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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