@inproceedings{
anonymous2023formal,
title={Formal Definition of Fingerprints Improves Attribution of Generative Models},
author={Anonymous},
booktitle={NeurIPS Workshop on Attributing Model Behavior at Scale},
year={2023},
url={https://openreview.net/forum?id=Zqj3YQ1QAC}
}
Recent works have shown that generative models leave traces of their underlying
generative process on the generated samples, broadly referred to as fingerprints of a
generative model, and have studied their utility in detecting synthetic images from
real ones. However, the extent to which these fingerprints can distinguish between
various types of synthetic images and help identify the underlying generative
process remain under-explored. In particular, the very definition of a fingerprint
remains unclear, to our knowledge. To that end, in this work, we formalize the
definition of artifact and fingerprint in generative models, propose an algorithm for
computing them in practice, and finally study how different design parameters affect
the model fingerprints and their attributability. We find that using our proposed
definition can significantly improve the performance on the task of identifying
the underlying generative process from samples (model attribution) compared to
existing methods. Additionally, we study the structure of the fingerprints and
observe that it is very predictive of the effect of different design choices on the
generative process.
Formal Definition of Fingerprints Improves Attribution of Generative Models