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.

本文提出了一种通过局部关系学习来检测面部伪造的新方法，其中使用了多尺度补丁相似性模块 (MPSM) 来测量局部区域特征的相似性，并形成了一个强大且泛化的相似性模式。此外，本文提出了 RGB-Frequency Attention Module (RFAM) 来融合 RGB 和频率领域的信息进行更全面的局部特征表示，进一步提高了相似性模式的可靠性。广泛的实验证明了该方法在广泛使用的基准测试中始终优于最先进的技术。此外，详细的可视化展示了我们方法的鲁棒性和可解释性。