A Large-scale Universal Evaluation Benchmark For Face Forgery Detection

摘要

With the rapid advancement of AIGC technology, realistic fake facial images and videos that deceive human perception are now possible. Consequently, numerous face forgery detection techniques have been proposed. However, evaluating their effectiveness and generalizability remains a significant challenge. To address this, we introduce DeepFaceGen, a large-scale benchmark for quantitatively assessing face forgery detection performance and supporting iterative advancements in the field. DeepFaceGen comprises 776,990 real face images/videos and 773,812 forged face samples generated using 35 mainstream face generation techniques. During its construction, we prioritized content diversity, ethnic fairness, and comprehensive labeling to ensure its versatility and usability. DeepFaceGen is then used to evaluate 20 leading face forgery detection methods from multiple perspectives. Through extensive analysis, we present key insights and propose directions for future research. The code and dataset for DeepFaceGen are available at https://github.com/HengruiLou/DeepFaceGen