Representation and Reference Selection in Training-Free Synthetic Image Attribution

Jul 15, 2026

A new preprint investigates how the choice of representation space and reference construction methods affect the performance of training-free synthetic image attribution. The study finds that attribution accuracy is highest when using intermediate layers of CLIP and DINOv2 models, and that semantically constrained references further improve results, particularly when only a small number of references are available. The analysis highlights the importance of both representation selection and reference construction in building effective attribution systems.

Why it matters: The findings offer practical insights for designing scalable, training-free attribution systems that can adapt to new image generators without retraining.

Full story at: arXiv Computer Vision ↗