Synthetic image attribution addresses the problem of tracing back the origin
of images produced by generative models. Extensive efforts have been made to
explore unique representations of generative models and use them to attribute a
synthetic image to the model that produced it. Most of the methods classify the
models or the architectures among those in a closed set without considering the
possibility that the system is fed with samples produced by unknown
architectures. With the continuous progress of AI technology, new generative
architectures continuously appear, thus driving the attention of researchers
towards the development of tools capable of working in open-set scenarios. In
this paper, we propose a framework for open set attribution of synthetic
images, named BOSC (Backdoor-based Open Set Classification), that relies on the
concept of backdoor attacks to design a classifier with rejection option. BOSC
works by purposely injecting class-specific triggers inside a portion of the
images in the training set to induce the network to establish a matching
between class features and trigger features. The behavior of the trained model
with respect to triggered samples is then exploited at test time to perform
sample rejection using an ad-hoc score. Experiments show that the proposed
method has good performance, always surpassing the state-of-the-art. Robustness
against image processing is also very good. Although we designed our method for
the task of synthetic image attribution, the proposed framework is a general
one and can be used for other image forensic applications.

基于背门攻击的开放集分类器的合成图像溯源方法 (BOSC)，通过注入类特定触发器来诱导网络建立类特征与触发特征的匹配，以此设计分类器并进行样本拒绝的方法。在实验证明该方法在合成图像溯源任务中表现良好，超过了最先进技术，并且对图像处理具有很好的鲁棒性，同时该框架还可应用于其他图像取证应用领域。

BOSC: 一个基于后门的开放集合合成图像归属框架

BOSC: A Backdoor-based Framework for Open Set Synthetic Image  Attribution

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.

由于缺乏在未知架构上工作的方法，使得大多数合成图像归因的方法只能归因于训练集中的模型或架构生成的图像，限制了其在实际场景中的适用性。本文提出了一个验证框架，该框架依赖于孪生网络，以解决将合成图像归因为生成它们的架构的开放集问题。我们考虑了两种不同的设置。第一种设置，系统确定两幅图像是否由同一生成架构产生。第二种设置，系统验证关于用于生成合成图像的架构的声明，利用由声称的架构生成的一个或多个参考图像。所提出系统的主要优势在于其能够在封闭集和开放集场景下运行，使得输入图像，无论是查询图像还是参考图像，都可以属于训练过程中考虑的架构或不属于它们。实验评估涵盖了各种生成架构，如 GAN、扩散模型和变压器，重点关注合成人脸图像生成，在封闭集和开放集设置下均证实了我们的方法的卓越性能和强大的泛化能力。