No abstract

Face recognition is one of the most important applications of MEC. However, there have been many fake face data that can deceive MEC devices, causing serious problems such as information leakage. Face forgery detection can effectively solve this problem. Current face forgery detection methods have achieved high accuracy. However, most of the methods are researched on the classification of face authenticity. We find that studying multi‐classification of forgery methods can not only improve the accuracy of the model to identify fake faces, but also help improve the generalization ability of fake face classification. We argue that multi‐scale features and high‐frequency features can expose more detailed forgery artifacts. So, we design four modules, which take advantage of the complementarity of RGB features and frequency features, global features and local features. The first module is a residual‐guided multi‐scale spatial attention module, which uses residuals to guide the RGB feature extractor to extract fake features from a multi‐scale perspective. The second module is a multi‐scale retinal feature extraction module. The third module is a multi‐scale channel attention‐guided local frequency statistics module, which extracts local frequency responses using sliding‐window DCT. The last module is a capsule network classification module with overall correlation to classify the fused features. The information transfer between the subject capsule and the classification capsule can increase the integrity of the model, making the model converge faster. We conduct experiments on the databases FaceForensics++, DeepfakeDetection, and FakeAVCeleb. Experimental result shows that our method performs well on forgery classification.