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 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.

中文翻译：

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基于孪生的合成图像开放集架构归属验证系统

尽管为合成图像归因开发了各种各样的方法，但大多数方法只能对训练集中包含的模型或架构生成的图像进行归因，并且不能与架构一起使用，从而阻碍了它们在现实场景中的适用性。在本文中，我们提出了一种依赖于暹罗网络的验证框架来解决合成图像的开放集归因于生成它们的架构的问题。我们考虑两种不同的设置。在第一种设置中，系统确定两个图像是否由相同的生成架构生成。在第二设置中，系统利用由所主张的架构生成的一个或多个参考图像来验证关于用于生成合成图像的架构的声明。该系统的主要优点是它能够在封闭和开放场景中运行，以便输入图像（无论是查询图像还是参考图像）都可以属于训练期间考虑的架构或不属于训练期间考虑的架构。实验评估涵盖各种生成架构（例如 GAN、扩散模型和 Transformer），重点关注合成人脸图像生成，证实了我们的方法在封闭和开放设置中的出色性能以及强大的泛化能力。