  The escalating quality of video generated by advanced video generation
methods results in new security challenges, while there have been few relevant
research efforts: 1) There is no open-source dataset for generated video
detection, 2) No generated video detection method has been proposed so far. To
this end, we propose an open-source dataset and a detection method for
generated video for the first time. First, we propose a scalable dataset
consisting of 964 prompts, covering various forgery targets, scenes, behaviors,
and actions, as well as various generation models with different architectures
and generation methods, including the most popular commercial models like
OpenAI's Sora and Google's Veo. Second, we found via probing experiments that
spatial artifact-based detectors lack generalizability. Hence, we propose a
simple yet effective \textbf{de}tection model based on \textbf{f}rame
\textbf{co}nsistency (\textbf{DeCoF}), which focuses on temporal artifacts by
eliminating the impact of spatial artifacts during feature learning. Extensive
experiments demonstrate the efficacy of DeCoF in detecting videos generated by
unseen video generation models and confirm its powerful generalizability across
several commercially proprietary models. Our code and dataset will be released
at \url{https://github.com/wuwuwuyue/DeCoF}.
