A multi-label classification method based on transformer for deepfake detection

With the continuous development of hardware and deep learning technologies, existing forgery techniques are capable of more refined facial manipulations, making detection tasks increasingly challenging. Therefore, forgery detection cannot be viewed merely as a traditional binary classification task. To achieve finer forgery detection, we propose a method based on multi-label detection classification capable of identifying the presence of forgery in multiple facial components. Initially, the dataset undergoes preprocessing to meet the requirements of this task. Subsequently, we introduce a Detail-Enhancing Attention Module into the network to amplify subtle forgery traces in shallow feature maps and enhance the network's feature extraction capabilities. Additionally, we employ a Global-Local Transformer Decoder to improve the network's ability to focus on local information. Finally, extensive experiments demonstrate that our approach achieves 92.45% mAP and 90.23% mAUC, enabling precise detection of facial components in images, thus validating the effectiveness of our proposed method.