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

TL;DR

This paper introduces a novel framework for detecting multi-face forgeries by learning facial relationships and aggregating features, addressing a gap in current single-face focused methods, and achieves state-of-the-art results.

Contribution

The paper presents a new multi-face forgery detection framework that combines facial relationship learning with global feature aggregation, filling a significant research gap.

Findings

Achieves state-of-the-art performance on multi-face forgery datasets.

Effectively distinguishes manipulated faces within complex images.

Enhances detection accuracy by leveraging mutual constraints between local and global features.

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.