Face Forgery Detection via Multi-Scale and Multi-Domain Features Fusion

Deepfake, as a popular form of visual forgery technique on the Internet, poses a serious threat to individuals' data privacy and security. In consumer electronics, fraudulent schemes leveraging Deepfake technology are widespread, making it urgent to safeguard users' data privacy and security. However, many Deepfake detection methods based on Convolutional Neural Networks (CNNs) struggle to achieve satisfactory performance on mainstream datasets, especially with heavily compressed images. Observing that tampered images leave traces in the frequency domain, which are imperceptible to the naked eye but detectable through spectrum analysis, this study proposes a novel face forgery detection framework integrating spatial and frequency domain features. The framework introduces three innovative modules: the cross-attention fusion module (CAFM), the guided attention module (GAM), and the multi-scale feature fusion module (MSFFM), Specifically, CAFM combines spatial and frequency-domain features through cross-attention to enhance feature interaction. GAM generates attention maps to refine the integration of spatial and frequency features, while MSFFM fuses multi-scale hierarchical features to capture both global and local tampering artifacts. These modules collectively improve the richness and discrimination of the extracted features, contributing to the overall detection performance. The proposed method demonstrates its effectiveness and superiority in forgery detection tasks, achieving a 3.9% average improvement in AUC compared to the state-of-the-art method GocNet [1] on FaceForensics++ (FF++) and WildDeepfake datasets. Extensive experiments further validate the effectiveness of our approach.