Deepfake Face Detection Algorithm Based on Multi-Scale Attention Reconstruction

Recent advances in deepfake facial technology have enabled its misuse for creating deceptive content and spreading false information, posing serious risks to personal privacy, social order, and national security. However, early deepfake detection methods fell short. For instance, the traditional reconstruction model couldn't adapt to data distribution changes, and the single-scale structure struggled to fully uncover various forgery artifacts. Therefore, we propose a deepfake face detection framework named Multi-scale Attention Reconstruction (MSAR). The framework reconstructs real faces to learn their feature distributions, enhancing detector generalization. Firstly, we introduce the adaptive neighborhood aggregation (ANA) module. It integrates information from adjacent regions at different scales and realizes selective feature fusion at the same scale, improving reconstruction quality. Moreover, we propose the attention collaborative guidance (ACG) module. It takes the mask difference between the reconstructed and source real-face images as input and captures long-range dependencies and local detail information. This guides the model to focus more on key features related to reconstruction errors, thus enhancing the classifier's performance. Experiments on public datasets such as FaceForensics++ and CelebDF show that MSAR outperforms existing methods in key metrics such as ACC and AUC. Ablation experiments also verify the effectiveness of each module.