VGMShield: Mitigating Misuse of Video Generative Models
Yan Pang Yang Zhang Tianhao Wang
With the rapid advancement in video generation, people can conveniently
utilize video generation models to create videos tailored to their specific
desires. Nevertheless, there are also growing concerns about their potential
misuse in creating and disseminating false information.
  In this work, we introduce VGMShield: a set of three straightforward but
pioneering mitigations through the lifecycle of fake video generation. We start
from \textit{fake video detection} trying to understand whether there is
uniqueness in generated videos and whether we can differentiate them from real
videos; then, we investigate the \textit{tracing} problem, which maps a fake
video back to a model that generates it. Towards these, we propose to leverage
pre-trained models that focus on {\it spatial-temporal dynamics} as the
backbone to identify inconsistencies in videos. Through experiments on seven
state-of-the-art open-source models, we demonstrate that current models still
cannot perfectly handle spatial-temporal relationships, and thus, we can
accomplish detection and tracing with nearly perfect accuracy.
  Furthermore, anticipating future generative model improvements, we propose a
{\it prevention} method that adds invisible perturbations to images to make the
generated videos look unreal. Together with fake video detection and tracing,
our multi-faceted set of solutions can effectively mitigate misuse of video
generative models.