With the rapid development of facial manipulation techniques, face forgery detection has received considerable attention in digital media forensics due to security concerns. Most existing methods formulate face forgery detection as a classification problem and utilize binary labels or manipulated region masks as supervision. However, without considering the correlation between local regions, these global supervisions are insufficient to learn a generalized feature and prone to overfitting. To address this issue, we propose a novel perspective of face forgery detection via local relation learning. Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions and forms a robust and generalized similarity pattern. Moreover, we propose an RGB-Frequency Attention Module (RFAM) to fuse information in both RGB and frequency domains for more comprehensive local feature representation, which further improves the reliability of the similarity pattern. Extensive experiments show that the proposed method consistently outperforms the state-of-the-arts on widely-used benchmarks. Furthermore, detailed visualization shows the robustness and interpretability of our method.

中文翻译：

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局部关系学习用于人脸伪造检测

随着面部操纵技术的迅速发展，由于安全方面的考虑，面部伪造检测在数字媒体取证中已受到相当大的关注。大多数现有方法将面部伪造检测公式化为分类问题，并利用二进制标签或可操作区域掩码作为监督。但是，如果不考虑本地区域之间的相关性，这些全球监管措施不足以学习通用特征，并且容易过拟合。为了解决这个问题，我们提出了一种通过局部关系学习进行人脸伪造检测的新视角。具体来说，我们提出了一种多尺度补丁相似度模块（MPSM），该模块可测量局部特征之间的相似度并形成鲁棒且通用的相似度模式。而且，我们提出了RGB频率注意模块（RFAM）来融合RGB和频域中的信息，以实现更全面的局部特征表示，从而进一步提高了相似性模式的可靠性。大量的实验表明，在广泛使用的基准测试中，所提出的方法始终优于最新技术。此外，详细的可视化显示了我们方法的鲁棒性和可解释性。