Venus-DefakerOne2: A Vision-Forensics Foundation Model with Broader Coverage
A vision-forensics model aims to cover digital and physical forgery in images and videos.
Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as physical spoofing, and is carried not only by images but also by videos.
ArXiv CS.CV
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Generated insight by OnAirToday AI
Forgery detection must account for both digitally altered content and physical spoofing across images and videos. A vision-forensics model with broader coverage could address a wider range of these cases.
Key takeaways
Generated insight by OnAirToday AI
- Venus-DefakerOne2: A Vision-Forensics Foundation Model with Broader Coverage
- Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as physical spoofing, and is carried not only by images but also by videos.
- A vision-forensics model aims to cover digital and physical forgery in images and videos.
Why it matters: Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as physical spoofing, and is carried not only by images but also by videos.
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