G2VD: Generalizable AI-Generated Video Detection via Counterfactual Intervention and Causal Disentanglement
The rapid advancement of AI-generated videos poses increasing security risks and calls for robust detectors with strong cross-domain generalization. Although existing methods achieve promising results under in-domain evaluation, their performance often degrades substantially when tested on unseen generators. A key reason is shortcut learning, where detectors rely on domain-specific spurious cues, such as generator-dependent fingerprints and generation styles, instead of intrinsic forgery traces. To address this issue, we propose G2VD, a Generalizable AI-Generated Video Detection framework base
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- PossiblePossibly related (embedding) · 47%Somnusochi/VLM-AutoYOLO →
- PossiblePossibly related (embedding) · 46%VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization - Apple Machine Learning Research →
- PossiblePossibly related (embedding) · 45%Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning →
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “G2VD: Generalizable AI-Generated Video Detection via Counter” ≈ “Developer-Y/cs-video-courses””
- LinkedLinked via arxiv author · 85%Meng Du →
“G2VD: Generalizable AI-Generated Video Detection via Counterfactual Intervention and Causal Disentanglement”
- LinkedLinked via arxiv author · 85%Hongchang Chen →
“G2VD: Generalizable AI-Generated Video Detection via Counterfactual Intervention and Causal Disentanglement”
- LinkedLinked via arxiv author · 85%Ran Li →
“G2VD: Generalizable AI-Generated Video Detection via Counterfactual Intervention and Causal Disentanglement”