# Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection Source: [Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:26:19.104Z Authors: Chenhao Lin, Fangbin Yi, Hang Wang, Qian Li, Deng Jingyi, Chao Shen Published: 2023-10-07T15:09:18.000Z ## Abstract Face forgery techniques have emerged as a forefront concern, and numerous detection approaches have been proposed to address this challenge. However, existing methods predominantly concentrate on single-face manipulation detection, leaving the more intricate and realistic realm of multi-face forgeries relatively unexplored. This paper proposes a novel framework explicitly tailored for multi-face forgery detection,filling a critical gap in the current research. The framework mainly involves two modules:(i) a facial relationships learning module, which generates distinguishable local features for each face within images,(ii) a global feature aggregation module that leverages the mutual constraints between global and local information to enhance forgery detection accuracy.Our experimental results on two publicly available multi-face forgery datasets demonstrate that the proposed approach achieves state-of-the-art performance in multi-face forgery detection scenarios. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Deepfake detection on OpenForensics (test)](): Deepfake Detection; OpenForensics (test); Accuracy: 92.04. ## Original sources and implementation [Original paper]() [PDF]()