GenWorld: Towards Detecting AI-generated Real-world Simulation Videos
Journal:
arXiv
Published Date:
Jun 12, 2025
Abstract
The flourishing of video generation technologies has endangered the
credibility of real-world information and intensified the demand for
AI-generated video detectors. Despite some progress, the lack of high-quality
real-world datasets hinders the development of trustworthy detectors. In this
paper, we propose GenWorld, a large-scale, high-quality, and real-world
simulation dataset for AI-generated video detection. GenWorld features the
following characteristics: (1) Real-world Simulation: GenWorld focuses on
videos that replicate real-world scenarios, which have a significant impact due
to their realism and potential influence; (2) High Quality: GenWorld employs
multiple state-of-the-art video generation models to provide realistic and
high-quality forged videos; (3) Cross-prompt Diversity: GenWorld includes
videos generated from diverse generators and various prompt modalities (e.g.,
text, image, video), offering the potential to learn more generalizable
forensic features. We analyze existing methods and find they fail to detect
high-quality videos generated by world models (i.e., Cosmos), revealing
potential drawbacks of ignoring real-world clues. To address this, we propose a
simple yet effective model, SpannDetector, to leverage multi-view consistency
as a strong criterion for real-world AI-generated video detection. Experiments
show that our method achieves superior results, highlighting a promising
direction for explainable AI-generated video detection based on physical
plausibility. We believe that GenWorld will advance the field of AI-generated
video detection. Project Page: https://chen-wl20.github.io/GenWorld
