{"ID":3053178,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-05T11:43:53.432517148Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04098","arxiv_id":"2606.04098","title":"When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection","abstract":"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.","short_abstract":"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 inp...","url_abs":"https://arxiv.org/abs/2606.04098","url_pdf":"https://arxiv.org/pdf/2606.04098v1","authors":"[\"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\"]","published":"2026-06-02T18:03:35Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}