Recent studies on face forgery detection have shown satisfactory performance for methods involved in training datasets, but are not ideal enough for unknown domains. This motivates many works to improve the generalization, but forgery-irrelevant information, such as image background and identity, still exists in different domain features and causes unexpected clustering, limiting the generalization. In this paper, we propose a controllable guide-space (GS) method to enhance the discrimination of different forgery domains, so as to increase the forgery relevance of features and thereby improve the generalization. The well-designed guide-space can simultaneously achieve both the proper separation of forgery domains and the large distance between real-forgery domains in an explicit and controllable manner. Moreover, for better discrimination, we use a decoupling module to weaken the interference of forgery-irrelevant correlations between domains. Furthermore, we make adjustments to the decision boundary manifold according to the clustering degree of the same domain features within the neighborhood. Extensive experiments in multiple in-domain and cross-domain settings confirm that our method can achieve state-of-the-art generalization.

翻译：近期关于人脸伪造检测的研究在训练数据集相关任务上表现良好，但面对未知域时效果仍不够理想。这促使众多研究着力提升泛化能力，然而不同域特征中仍存在图像背景、身份等与伪造无关的信息，导致意外聚类问题，限制了泛化性能。本文提出可控引导空间（GS）方法，增强对不同伪造域的区分能力，从而提升特征的伪造关联性并改善泛化效果。精心设计的引导空间能够以显式可控的方式同时实现伪造域间的合理分离与真假域间的大间距。此外，为提升判别性能，我们采用解耦模块削弱域间伪造无关相关性的干扰。进一步地，我们根据邻域内同类域特征的聚类程度调整决策边界流形。在多项域内与跨域场景下的广泛实验表明，本方法可取得最先进的泛化性能。