Scalable Black-Box Model Attribution for Images
The rapid proliferation of generative models raises the model attribution problem: given only an image, can we determine which model produced it? We propose a lightweight CNN to solve this problem in a strict black box setting. The CNN operates on multiple image patches to handle varying image size and improve accuracy. It attributes more models at higher accuracy than prior work, reaching 98.9% on 25-class DRAGON and 95.0% on 27-class OpenFake; runs in a few milliseconds at a cost nearly independent of candidate-set size; and remains robust to transformations encountered in the wild. Beyond closed-set attribution, its learned representation serves as a reusable fingerprint backbone: it s...
| Authors | Asaf Livne, Amir Jevnisek, Shai Avidan |
|---|---|
| Primary category | cs.CV |
| Published | 16 August 2026 |
| Last updated at source | 20 September 2026 |
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