← 研踪 PaperTrailarXiv:2610.36933 · v1
Provenance Signatures for Low Rank Adaptations of Image Diffusion Models
首次提交 2026-10-27 17:58
版本更新 2026-10-27 17:58
雷达观测 2026-10-29 00:35
摘要
Diffusion models have revolutionized the generation of realistic artificial images. Efficient techniques such as Low Rank Adaptation (LoRA) can modify these models for particular use cases, making them more diverse and widely usable. At the same time, open base models may be adapted to deliver biased, harmful or illegal images in an online service. In this paper, we develop a protocol to detect when a service may be using an adaptation of an otherwise standard model. We describe the precise setup and threat model for this provenance question and define two different problems arising due the presence of multiple model families and multiple adaptations. In contrast to previous works that have considered model identification within lists of known candidate models, we consider the realistic open world scenario where the set of possible adaptations and alternative models are unknown. Using a hypothesis testing approach, our protocols can distinguish between the base model, a LoRA adaptation and other completely different families of models. For this test, we develop a black box signature of the models based only on intrinsic dimension of sets of images generated by the models.
作者备注：Under review
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-10-27 17:58 · 最新观测版本
Provenance Signatures for Low Rank Adaptations of Image Diffusion Models
Diffusion models have revolutionized the generation of realistic artificial images. Efficient techniques such as Low Rank Adaptation (LoRA) can modify these models for particular use cases, making them more diverse and widely usable. At the same time, open base models may be adapted to deliver biased, harmful or illegal images in an online service. In this paper, we develop a protocol to detect when a service may be using an adaptation of an otherwise standard model. We describe the precise setup and threat model for this provenance question and define two different problems arising due the presence of multiple model families and multiple adaptations. In contrast to previous works that have considered model identification within lists of known candidate models, we consider the realistic open world scenario where the set of possible adaptations and alternative models are unknown. Using a hypothesis testing approach, our protocols can distinguish between the base model, a LoRA adaptation and other completely different families of models. For this test, we develop a black box signature of the models based only on intrinsic dimension of sets of images generated by the models.
查看此版本解读 →查看此版本原文 ↗