  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.
