Face Forgery Detection Based On Segmentation Network

Recent progress in facial manipulation technologies have made it hard to distinguish the sophisticated face swapped images/videos. Due to the diversity of generation software and data sources, it is extremely challenging to devise an efficient generality framework. Instead of regarding the detection process as a vanilla binary classification task, we proposed a detection framework based on pixel-level classification. Considering that the acquisition of real pixel-level ground-truth is somehow expensive or even impractical, we proposed a pseudo ground-truth generation pipeline with prior knowledge of facial manipulation. Besides, we added a new module into the neural network to capture frequency clues, while the ablation experiment verified the effectiveness of this module. The experimental results on several public datasets demonstrated that our proposed framework is effective and superior to other existing similar detection networks.