The emergence of advanced Deepfake technologies has gradually raised concerns in society, prompting significant attention to Deepfake detection. However, in real-world scenarios, Deepfakes often involve multiple faces. Despite this, most existing detection methods still detect these faces individually, overlooking the informative correlation between them and the relationship between the global information of the image and the local information of the faces. In this paper, we address this limitation by proposing FILTER, a novel framework for multi-face forgery detection that explicitly captures underlying correlations. FILTER consists of two main modules: Multi-face Relationship Learning (MRL) and Global Feature Aggregation (GFA). Specifically, MRL learns the correlation of local facial features in multi-face images, and GFA constructs the relationship between image-level labels and individual facial features to enhance performance from a global perspective. In particular, a contrastive learning loss function is used to better discriminate between real and fake faces. Extensive experiments on two publicly available multi-face forgery datasets demonstrate the state-of-the-art performance of FILTER in multi-face forgery detection. For example, on Openforensics Test-Challenge dataset, FILTER outperforms the previous state-of-the-art methods with a higher AUC score (0.980) and higher detection accuracy (92.04%).

中文翻译：

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利用面部关系和特征聚合进行多脸伪造检测


Deepfake先进技术的出现逐渐引起社会关注，促使Deepfake检测受到高度重视。然而，在现实场景中，Deepfakes 通常涉及多个面孔。尽管如此，大多数现有的检测方法仍然单独检测这些人脸，忽略了它们之间的信息相关性以及图像的全局信息和人脸局部信息之间的关系。在本文中，我们通过提出 FILTER 来解决这一限制，FILTER 是一种用于多面伪造检测的新颖框架，可以显式捕获潜在的相关性。 FILTER由两个主要模块组成：多脸关系学习（MRL）和全局特征聚合（GFA）。具体来说，MRL学习多脸图像中局部面部特征的相关性，GFA构建图像级标签和个体面部特征之间的关系，以从全局角度增强性能。特别是，使用对比学习损失函数来更好地区分真脸和假脸。对两个公开可用的多脸伪造数据集进行的广泛实验证明了 FILTER 在多脸伪造检测中的最先进的性能。例如，在 Openforensics Test-Challenge 数据集上，FILTER 优于之前最先进的方法，具有更高的 AUC 分数（0.980）和更高的检测准确率（92.04%）。