  Despite the wide variety of methods developed for synthetic image
attribution, most of them can only attribute images generated by models or
architectures included in the training set and do not work with unknown
architectures, hindering their applicability in real-world scenarios. In this
paper, we propose a verification framework that relies on a Siamese Network to
address the problem of open-set attribution of synthetic images to the
architecture that generated them. We consider two different settings. In the
first setting, the system determines whether two images have been produced by
the same generative architecture or not. In the second setting, the system
verifies a claim about the architecture used to generate a synthetic image,
utilizing one or multiple reference images generated by the claimed
architecture. The main strength of the proposed system is its ability to
operate in both closed and open-set scenarios so that the input images, either
the query and reference images, can belong to the architectures considered
during training or not. Experimental evaluations encompassing various
generative architectures such as GANs, diffusion models, and transformers,
focusing on synthetic face image generation, confirm the excellent performance
of our method in both closed and open-set settings, as well as its strong
generalization capabilities.
