When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection
A new benchmark, EVID-Bench, assesses search-based video misinformation detection. It includes 222 videos with 9 manipulation types in 3 categories. The best model achieves only 61.43% point-level and 43.24% video-level accuracy, with AI-generated manipulations being especially challenging.
[2606.04098] When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection
[Submitted on 2 Jun 2026]
Title:When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection
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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.
Comments: 52 pages
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.04098 [cs.CV]
(or arXiv:2606.04098v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.04098
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tao Yu [view email] [v1] Tue, 2 Jun 2026 18:03:35 UTC (32,252 KB)
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