Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection
This addresses a critical gap in face forgery detection for realistic scenarios involving multiple faces, though it is incremental as it builds on existing detection methods.
The paper tackles the problem of detecting manipulated images with multiple faces, which is more complex than single-face detection, and achieves state-of-the-art performance on two public datasets.
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.