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. Despite substantial p
Venus-DefakerOne2 is a unified anti-forgery foundation model that extends detection to diverse forgery forms and media types through alternating LoRA and full-parameter tuning and entropy-based test adaptation.
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. Despite substantial progress, a unified anti-forgery foundation model covering such a broad threat spectrum remains absent. In this paper, we present Venus-DefakerOne2, which substantially extends the forensic capability boundary along both the forgery-form and media-type dimensions. Trained on tens of millions of open-source and in-house samples, it incorporates two key designs. Tide-Style-Tuning alternates LoRA and full-parameter fine-tuning to balance computational efficiency, generalization, and hard-sample…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Venus-DefakerOne2: A Vision-Forensics Foundation Model with Broader Coverage was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 13 checks on 2 different days. Its best position was #10 of 30, reached on Nov 3, 11:28 UTC. It dropped off the list after Nov 3, 11:28 UTC.
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#10 on Hugging Face · Daily papers, on Nov 3, 11:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 2, 2026 and has been on the list on 2 days (13 checks) since.