G2VD: Generalizable AI-Generated Video Detection via Counterfactual Intervention and Causal Disentanglement
arXiv:2607.04607v1 Announce Type: cross Abstract: 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 General
The proliferation of sophisticated AI-generated videos necessitates advanced detection methods to mitigate growing security risks and counter deepfake capabilities, driving rapid research in this area.
Robust AI-generated video detection is crucial for maintaining trust in digital media, combating misinformation, and securing digital identities against escalating AI-powered manipulation.
The ability to accurately differentiate between real and AI-generated video content, even from unseen generators, will significantly improve; this is currently a major weakness.
- · Digital forensics companies
- · Social media platforms
- · Cybersecurity sector
- · Fact-checking organizations
- · Malicious actors using deepfakes
- · Propaganda networks
- · Individuals vulnerable to identity manipulation
Improved detection capabilities will make it harder to deploy convincing AI-generated video for malicious purposes.
Public trust in online video content may incrementally increase as detection technology becomes more robust and widespread.
The arms race between AI generation and detection will intensify, potentially leading to more sophisticated and harder-to-detect fakes, or AI-powered countermeasures becoming standard infrastructure.
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Read at arXiv cs.AI