Mining Fine-Grain Face Forgery Cues with Fusion Modality

Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns. Despite the considerable progress in existing methods, we note that: Previous works overlooked fine-grain forgery cues with high transferability. Such cues positively impact the model’s accuracy and generalizability. Moreover, single-modality often causes overfitting of the model, and Red-Green-Blue (RGB) modal-only is not conducive to extracting the more detailed forgery traces. We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues. First, we propose two functional modules to reveal and locate the deeper forged features. Our method locates deeper forgery cues through a dual-modality progressive fusion module and a noise adaptive enhancement module, which can excavate the association between dual-modal space and channels and enhance the learning of subtle noise features. A sensitive patch branch is introduced on this foundation to enhance the mining of subtle forgery traces under fusion modality. The experimental results demonstrate that our proposed framework can desirably explore the differences between authentic and forged images with supervised learning. Comprehensive evaluations of several mainstream datasets show that our method outperforms the state-of-the-art detection methods with remarkable detection ability and generalizability.