HuggingFace Daily Papers·· 1 天前
Verus-Insight：面向 AI 生成视频的可解释伪影检测
Verus-Insight: Towards Explainable Artifact Detection in AI-Generated Videos
AI 导读
Verus-Insight 将 AI 生成视频的伪影检测拆成检查规划、证据验证和证据聚合三个能力，用人工锚定的全流程监督和渐进式能力训练提升细粒度伪影识别与视频级检测。团队同时发布带细粒度人工伪影标注的 Verus-Bench，实验显示其在恢复人类可感知伪影上有明显提升，并能更可靠地把检查目标转化为有依据的伪影证据，而不增加虚假伪影声称。
正文 · 原文
Authors: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
Abstract:Recent advances in video generation have made synthetic videos increasingly realistic, placing greater demands on reliable AI-generated video detection. Existing artifact-based detectors typically identify generation artifacts as evidence for source prediction, yet they may either fail to recognize subtle artifacts or reach the correct source decision without faithfully identifying the evidence underlying it. In this work, we systematically investigate how to design and train effective artifact detectors and formulate artifact detection as a structured process comprising three complementary capabilities: Inspection Planning, which determines what should be examined; Evidence Verification, which verifies whether the inspected content exhibits genuine generation artifacts; and Evidence Aggregation, which integrates the verified evidence into the final source decision. Building on this formulation, we introduce Verus-Insight, trained with human-anchored full-process supervision and progressive capability training to strengthen both fine-grained artifact detection and video-level detection. We further introduce Verus-Bench, a benchmark with fine-grained human annotations of perceptible artifacts for evaluating artifact detection. Extensive experiments across diverse benchmarks demonstrate that Verus-Insight achieves strong performance in both fine-grained artifact analysis and AI-generated video detection, with substantial gains in recovering human-perceived artifacts. Further analyses reveal that the three capabilities are complementary yet distinct, and that Verus-Insight more reliably converts relevant inspection targets into supported artifact evidence without increasing spurious artifact claims.
| Comments: | 35 pages |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.30866 [cs.CV] |
| (or arXiv:2610.30866v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.30866 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yang Shi [view email]
[v1]
Thu, 22 Oct 2026 07:44:36 UTC (5,983 KB)
来源：HuggingFace Daily Papers · arxiv.org