  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.
