[Submitted on 13 Jul 2026]
Title:Representation and Reference Selection in Training-Free Synthetic Image Attribution
View a PDF of the paper titled Representation and Reference Selection in Training-Free Synthetic Image Attribution, by Meiling Li and 3 other authors
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Abstract:Synthetic image attribution aims at identifying the generator responsible for a given AI-generated image. Training-free reference-based attribution methods are easily scalable, since newly emerging generators can be incorporated by adding source-specific references rather than retraining a task-specific classifier. Their performance depends on two coupled factors: the representation space used for comparison and the way source-specific references are constructed. However, the interaction between these two factors remains largely unexplored. In this paper, we provide a controlled analysis of this interaction using references and off-the-shelf pretrained representations. We study representations extracted from different layers of CLIP and DINOv2, along with three reference selection methods with varying semantic constraints: arbitrary, semantically aligned, and resynthesis-based references. Our results show that attribution accuracy consistently peaks at intermediate representation levels, indicating that source-discriminative cues are more accessible before strong semantic abstraction dominates. We further show that intermediate representations are not completely semantically neutral, making reference selection critical: semantically constrained references reduce query-reference mismatch and improve attribution, especially under limited reference budgets. Resynthesis is most useful in low-reference regimes, while semantically aligned references provide a better accuracy-cost trade-off when a moderate-sized reference pool is available. Our findings show that training-free reference-based attribution should be understood as the interaction between where images are compared, how the reference set is constructed, and how many references are available.
Comments: 6 pages, 5 figures, 4 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
Cite as: arXiv:2607.12052 [cs.CV]
(or arXiv:2607.12052v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.12052
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Meiling Li [view email] [v1] Mon, 13 Jul 2026 18:10:39 UTC (1,130 KB)
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