  With the rapid development of AI-generated content (AIGC) technology, the
production of realistic fake facial images and videos that deceive human visual
perception has become possible. Consequently, various face forgery detection
techniques have been proposed to identify such fake facial content. However,
evaluating the effectiveness and generalizability of these detection techniques
remains a significant challenge. To address this, we have constructed a
large-scale evaluation benchmark called DeepFaceGen, aimed at quantitatively
assessing the effectiveness of face forgery detection and facilitating the
iterative development of forgery detection technology. DeepFaceGen consists of
776,990 real face image/video samples and 773,812 face forgery image/video
samples, generated using 34 mainstream face generation techniques. During the
construction process, we carefully consider important factors such as content
diversity, fairness across ethnicities, and availability of comprehensive
labels, in order to ensure the versatility and convenience of DeepFaceGen.
Subsequently, DeepFaceGen is employed in this study to evaluate and analyze the
performance of 13 mainstream face forgery detection techniques from various
perspectives. Through extensive experimental analysis, we derive significant
findings and propose potential directions for future research. The code and
dataset for DeepFaceGen are available at
https://github.com/HengruiLou/DeepFaceGen.
