Common Forgery Artifact Driven Deepfake Face Detection

Given the substantial security risks associated with Deepfake technology, the identification of manipulated facial images has become a focal point of research. Regrettably, the majority of current Deepfake detection methods struggle to effectively discern forgery artifacts across various resolutions. Variations in image or video resolutions present substantial challenges to maintaining identity security in cooperative work environments. In this study, we introduce a Deepfake face detection model that relies on the identification of common forgery artifacts. Our model utilizes CFNet (Common Forgery Artifact Extraction Network) to automatically filter regions containing forged artifacts. These common forged artifacts are found in images of various resolutions, substantially enhancing the model's accuracy in low-resolution images. Furthermore, our customdesigned multi-modal features ensure the model excels in high-resolution scenarios. Comprehensive experiments validate the efficacy of our model, achieving accuracy rates of 90.464% for Deepfakes, 75.520% for Face2Face, and 83.536% for FaceSwap within the Low Quality (LQ) category of the FF+ dataset.