RAIRNet: Region-Aware Identity Rectification for Face Forgery Detection

The malicious usage of facial manipulation techniques boosts the desire of face forgery detection research. Recently, identity-based approaches have attracted much attention due to the effective observation of identity inconsistency. However, there are still several nonnegligible problems: (1) generic identity extractor is totally trained on real images, leading to enormous identity representation bias during processing forged content; (2) the identity information of forged image is hybrid and presents regional distribution, while the single global identity feature is hard to reflect this local identity inconsistency. To solve the above problems, in this paper a novel Region-Aware Identity Rectification Network (RAIRNet) is proposed to effectively rectify the identity bias and adaptively exploit the inconsistency local region. Firstly, for the identity bias problem, our RAIRNet is devised in a two-branch architecture, which consists of a Generic Identity Extractor (GIE) branch and a Bias Diminishing Module (BDM) branch. The BDM branch is designed to rectify the bias introduced by GIE branch through a prototype-based training schema. This two-branch architecture effectively promotes model to adapt to forged content while maintaining the focus on identity space. Secondly, for local identity inconsistency exploiting, a novel Meta Identity Filter Generator (MIFG) is devised in a meta-learning way to generate the region-aware filter based on identity prior. This region-aware filter can adaptively exploit the local inconsistency clues and activate the discriminative local region. Moreover, to balance the local-global information and highlight the forensic clues, an Adaptive Weight Assignment Mechanism (AWAM) is proposed to assign adaptive importance weight to two branches. Extensive experiments on various datasets show the superiority of our RAIRNet. In particular, on the challenging DFDCp dataset, our approach outperforms previous binary-based and identity-based methods by 10.3% and 5.5% respectively.