Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection

• URL: http://arxiv.org/abs/2310.04845v1
• Date: Sat, 7 Oct 2023 15:09:18 GMT
• Title: Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection
• Authors: Chenhao Lin, Fangbin Yi, Hang Wang, Qian Li, Deng Jingyi, Chao Shen
• Abstract summary: 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. 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.
• Score: 21.976412231332798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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