Scalable Black-Box Model Attribution for Images

Asaf Livne; Amir Jevnisek; Shai Avidan

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arXiv · 2608.15652

Scalable Black-Box Model Attribution for Images

Asaf Livne·Amir Jevnisek·Shai Avidan

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Abstract

The rapid proliferation of generative models raises the model attribution problem: given only an image, can we determine which model produced it? Existing methods have grown as elaborate as the generators they target, on the as- sumption that a more sophisticated model demands a more sophisticated attributor. We show it does not. RPA (Raw- Patch Attribution) attributes images in the strictest black- box setting with a lightweight CNN. Despite its simplicity, it attributes more models at higher accuracy than prior work, reaching 98.0% on 25-class DRAGON and 92.9% on 27- class OpenFake; it is data-efficient and runs at a cost inde- pendent of the number of candidate models; and it stays ro- bust to the compression, blur, and resizing images undergo in the wild. Training for closed-set attribution yields a ver- satile feature extractor: the same representation recovers model lineage without supervision, flags and groups unseen generators, and admits new models through few-shot adap- tation rather than retraining.

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Asaf Livne, Amir Jevnisek, Shai Avidan. 2026-08-16. Scalable Black-Box Model Attribution for Images. https://arxiv.org/abs/2608.15652

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