  The generative model has made significant advancements in the creation of
realistic videos, which causes security issues. However, this emerging risk has
not been adequately addressed due to the absence of a benchmark dataset for
AI-generated videos. In this paper, we first construct a video dataset using
advanced diffusion-based video generation algorithms with various semantic
contents. Besides, typical video lossy operations over network transmission are
adopted to generate degraded samples. Then, by analyzing local and global
temporal defects of current AI-generated videos, a novel detection framework by
adaptively learning local motion information and global appearance variation is
constructed to expose fake videos. Finally, experiments are conducted to
evaluate the generalization and robustness of different spatial and temporal
domain detection methods, where the results can serve as the baseline and
demonstrate the research challenge for future studies.
