关键词: 人脸伪造检测；原型相似度；特征融合；注意力机制；泛化能力

Abstract: Most existing deepfake face detection methods rely on specific forgery patterns, such as noise artifacts or local textures, making them highly dependent on known forged features and lacking generalization to unknown forgeries. To address this issue, this paper proposes a multiloss fusion detection framework based on the reconstructionclassification learning(RECCE) network. A prototype similarity mechanism is introduced to measure the distance between sample features and class prototypes, enhancing the model’s ability to detect unknown forgeries. A joint loss function combining prototype loss, binary crossentropy loss, reconstruction loss, and metric learning loss is designed to strengthen feature learning from multiple perspectives. Moreover, multilevel encoder features are fused, and a reconstructionguided attention mechanism focuses the model on forged regions rather than the entire face, improving robustness and accuracy. Experiments conducted on several benchmark datasets and compared with six stateoftheart methods demonstrate significant improvements: training time is reduced to 28% of the original model, and the AUC increases by 1.48% in crossdomain evaluation when trained on FaceForensics++ (c40) and tested on CelebDF. The results verify the superior performance and generalization ability of the proposed method.

Key words: deepfake face detection; prototype similarity; feature fusion; attention mechanism; generalization ability