The escalating quality of video generated by advanced video generation
methods leads to new security challenges in society, which makes generated
video detection an urgent research priority.To foster collaborative research in
this area, we construct the first open-source dataset explicitly for generated
video detection, providing a valuable resource for the community to benchmark
and improve detection methodologies. Through a series of carefully designed
probe experiments, our study explores the significance of temporal and spatial
artifacts in developing general and robust detectors for generated video. Based
on the principle of video frame consistency, we introduce a simple yet
effective detection model (DeCoF) that eliminates the impact of spatial
artifacts during generalizing feature learning. Our extensive experiments
demonstrate the efficacy of DeCoF in detecting videos produced by unseen video
generation models and confirm its powerful generalization capabilities across
several commercial proprietary models.

通过开发开源数据集和基于视频帧一致性原则的简单但有效的检测模型，研究论文更好地探索了生成视频检测的挑战和方法，以提高检测效果和推广能力。