見ることが信じることではない ― 検索基盤型動画誤情報検出のベンチマーク
When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection
動画の誤情報を検出するため、ウェブ検索で関連動画を探し比較するベンチマークEVID-Benchを構築し、最先端モデルの性能が低いことを示した。
著者: Tao Yu, Yujia Yang, Shenghua Chai, Zhang Jinshuai, Haopeng Jin, Hao Wang, Minghui Zhang, Zhongtian Luo, Yuchen Long, Xinlong Chen, Jiabing Yang, Zhaolu Kang, Yuxuan Zhou, Zhengyu Man, Xinming Wang, Hongzhu Yi, Zheqi He, Xi Yang, Yan Huang, Liang Wang
分類: cs.CV
原文アブストラクト
Video misinformation increasingly operates at the semantic and evidential level: authentic footage may be selectively edited, temporally reordered, spliced across sources, or augmented with AI-generated content to construct false narratives. Such evidence-dependent manipulations cannot be reliably verified from the input video alone, because the missing, reordered, replaced, or recontextualized evidence lies outside the video itself. We introduce \textbf{EVID-Bench}, a benchmark for search-grounded video misinformation detection, where a system must search the open web for related videos and identify what information is false through cross-video comparison. EVID-Bench comprises 222 videos spanning 9 manipulation types across 3 categories: AI generation, single-source editing, and multi-source editing. All samples are verified to be undetectable by frontier models through visual inspection alone. We evaluate nine frontier multimodal models using a retrieval-augmented verification baseline. The best system achieves only 61.43\% point-level accuracy and 43.24\% video-level accuracy, while AI-generated manipulations remain especially challenging. Error analysis reveals recurring challenges: models fixate on irrelevant anchors, misattribute synthetic content to editorial splicing, and terminate search prematurely before fully explaining the manipulation.