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Mitigating Misuse of Video Generative Models A Multi-Faceted Approach through Fake Video Detection, Tracing, and Prevention
Abstract:
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 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 fake video source 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 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 source tracing with nearly perfect accuracy.
Furthermore, anticipating future generative model improvements, we propose a prevention method that adds invisible perturbations to images to make the generated videos look unreal.
Committee:
- David Evans, Committee Chair (CS/SEAS/UVA)
- Tianhao Wang, Advisor (CS/SEAS/UVA)
- Jundong Li (CS, ECE/SEAS, SDS/UVA)
- Miaomiao Zhang (ECE,CS/SEAS/UVA)