DeepFake Detection Method Based on Multi-Scale Dual-Stream Network

DeepFake-enabled abuse of face forgery technology has given rise to considerable security risks to society and individuals; therefore, DeepFake detection has become a hot topic of research. Current deep learning-based forgery detection techniques exhibit good results on High-Quality (HQ) datasets but show poor performance on Low-Quality (LQ) datasets and across different datasets. To improve the generalization of DeepFake detection performance, this paper proposes a Multi-Scale Dual-Stream Network (MSDSnet) for DeepFake detection. The network input is divided into a spatial-domain feature stream and a high-frequency noise feature flow. First, the Multi-Scale Fusion (MSF) module is used to capture the tampered coarse-grain facial features from images and fine-grained high-frequency noise information from forged images in different situations. The network fully integrates the dual-stream features of the spatial-domain feature stream and high-frequency noise feature flow through the MSF module. The Multi-modal Interaction Attention (MIA) module further interacts to learn the dual-stream information. Finally, a Frequency Channel Attention Network (FcaNet) is used to obtain the global information of the forged face features for complete detection and classification. Experimental results show that the proposed method achieves 98.54% accuracy on the HQ dataset Celeb-DF v2 and 93.11% on the LQ dataset FaceForensics++. Simultaneously, the experimental results are better than those obtained using other methods in cross-dataset experiments.