Representation and Reference Selection in Training-Free Synthetic Image Attribution

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.