Frequency-Aware Attentional Feature Fusion for Deepfake Detection

Various face manipulation techniques develop rapidly and can easily generate high-quality fake images or videos, posing significant ethical concerns when used for malicious purposes. Although recent works achieve significant performance in deepfake detection, they still suffer from overfitting issues. To deal with this problem, we propose a novel framework to aggregate diverse information for deepfake detection from both RGB and frequency. Specially, we first introduce a channel attention module to assemble local and global contexts to overcome the potential semantic inconsistency on local artifacts and global features. Then we design a spatial-frequency feature fusion module to fuse the RGB-frequency information comprehensively. Moreover, a variant attention module is further proposed to improve feature discrimination. Extensive experiments demonstrate that our method maintains comparable performance in intra-dataset and cross-dataset evaluation.