Local Relation Learning for Face Forgery Detection

Understand

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.