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arXiv Computer Vision By Zhuoran Zhang, Yang Shi, Qixun Wang, Tengfei Liu, Yuqi Tang, Xuanyu Zhu, Yizhuo Jia, Jingyun Hua, Shixuan Liu, Xianghua Ying, Haotian Wang, Yuanxing Zhang, Pengfei Wan, Wenjing Yang

Verus-Insight: Towards Explainable Artifact Detection in AI-Generated Videos

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Verus-Insight is a new artifact detection framework for AI‑generated videos that treats detection as a structured process with three complementary capabilities: Inspection Planning, Evidence Verification, and Evidence Aggregation. The system is trained using human‑anchored full‑process supervision and progressive capability training, enabling it to perform fine‑grained artifact analysis and robust video‑level source prediction. A new benchmark, Verus‑Bench, provides fine‑grained human annotations of perceptible artifacts, and experiments show that Verus‑Insight outperforms existing methods in both artifact recovery and overall detection accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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