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.
@article{arxiv.2310.04845,
title = {Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection},
author = {Chenhao Lin and Fangbin Yi and Hang Wang and Qian Li and Deng Jingyi and Chao Shen},
journal= {arXiv preprint arXiv:2310.04845},
year = {2023}
}