# When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection

arXiv: https://arxiv.org/abs/2606.04098 · HTML: https://mlsanity.com/paper/2606.04098/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues](https://mlsanity.com/paper/2007.09355/) (2020)
- [Seedance 2.0: Advancing Video Generation for World Complexity](https://mlsanity.com/paper/2604.14148/) (2026)
- [DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation](https://mlsanity.com/paper/2010.00400/) (2020)
- [COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning](https://mlsanity.com/paper/2101.06278/) (2021)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [Text-Video Multi-Grained Integration for Video Moment Montage](https://mlsanity.com/paper/2412.09276/) (2024)
- [ShotFinder: Imagination-Driven Open-Domain Video Shot Retrieval via Web Search](https://mlsanity.com/paper/2601.23232/) (2026)
- [Beyond Closed-Pool Video Retrieval: A Benchmark and Agent Framework for Real-World Video Search and Moment Localization](https://mlsanity.com/paper/2602.10159/) (2026)
- [DF-Net: The Digital Forensics Network for Image Forgery Detection](https://mlsanity.com/paper/2503.22398/) (2025)
- [Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation](https://mlsanity.com/paper/2601.06600/) (2026)
- [Official-NV: An LLM-Generated News Video Dataset for Multimodal Fake News Detection](https://mlsanity.com/paper/2407.19493/) (2024)
- [MERIT: Modular Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning](https://mlsanity.com/paper/2510.17590/) (2025)
- [Exposing Cross-Modal Consistency for Fake News Detection in Short-Form Videos](https://mlsanity.com/paper/2603.14992/) (2026)

## Built on by (most cited first, 2 of 2)

- [CM2: Multimodal Cultural Reasoning via an Integrated Multi-Agent Framework](https://mlsanity.com/paper/2608.30498/) (2026)
- [SAVOR: Self-Aware Visual Grounding via Confidence-Calibrated Reinforcement Learning for Multimodal Hallucination Mitigation](https://mlsanity.com/paper/2609.16601/) (2026)

## Abstract (arXiv)

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 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.