arXiv 2610.30866 · cs.CV · 2026-10-22
Verus-Insight: Towards Explainable Artifact Detection in AI-Generated Videos
Zhuoran Zhang, Yang Shi, Qixun Wang, Tengfei Liu, Yuqi Tang, Xuanyu Zhu, Yizhuo Jia, Jingyun Hua 等 14 人
机构待核实
在最新榜 #357 本站收录于 2026-10-26
一句话要点
Verus-Insight 把 AI 生成视频的伪影检测拆成三步：决定检查什么、验证是否真是生成伪影、再汇总证据下判断，目标是让检测结论有可解释的依据。
Verus-Insight frames explainable artifact detection in AI-generated videos as inspection planning, evidence verification and evidence aggregation, so source decisions rest on faithfully identified artifacts.
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主题与学科
同主题：视觉理解与 3D
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- EgoXperience: Learning World-Grounded Egocentric Representations from Human Experience ▲189
- Holi-World: Building Holistic World Intelligence from Human-Ego Experience ▲172
- DONIC: Generalist Dexterous Bimanual Tracking Control from Human Demonstrations ▲161
同主题：评测与数据
- StreamEval: Evaluating Interactive Streaming Video Generation Models 2026-10-20 · ▲649
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- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations 2026-10-01 · ▲287
- FDE-Gym: The Last Mile of AI Delivery 2026-10-15 · ▲270
数据说明
元数据来自 arXiv API（已核实）；点赞来自 Hugging Face 每日论文；引用来自 Semantic Scholar。最近刷新 2026-10-26。
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