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.

翻译：尽管针对合成图像溯源已开发出多种方法，但大多数方法仅能识别训练集中包含的模型或架构生成的图像，无法处理未知架构，这限制了其在实际场景中的应用。本文提出一种基于孪生网络的验证框架，用于解决合成图像开放集溯源中生成架构识别问题。我们考虑两种设置：第一种设置中，系统判断两幅图像是否由同一生成架构产生；第二种设置中，系统利用声称架构生成的一幅或多幅参考图像，验证关于合成图像生成架构的声称。本系统的主要优势在于其能够同时在封闭集和开放集场景下运行——输入图像（包括查询图像和参考图像）可以属于训练时涉及的架构，也可以不属于。针对包括生成对抗网络、扩散模型和Transformer在内的多种生成架构（聚焦于人脸图像合成）的实验评估证实，本方法在封闭集和开放集场景下均表现出色，并具备强大的泛化能力。