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

• URL: http://arxiv.org/abs/2606.04098v1
• Date: Tue, 02 Jun 2026 18:03:35 GMT
• Title: When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection
• Abstract summary: EVID-Bench is a benchmark for search-grounded video misinformation detection.<n>It comprises 222 videos spanning 9 manipulation types across 3 categories: AI generation, single-source editing, and multi-source editing.<n>The best system achieves only 61.43% point-level accuracy and 43.24% video-level accuracy, while AI-generated manipulations remain especially challenging.
• Score: 21.933691657055075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• R^3: Composed Video Retrieval via Reasoning-Guided Recalling and Re-ranking [73.0537447183962]
  We present a zero-shot composed video retrieval pipeline built around Reasoning-guided Recalling and Reranking.<n>The model generates a reasoning trace that describes the expected target video after applying the edit.<n>Experiments have demonstrated the effectiveness of our method in addressing this challenge.
  arXiv  Detail & Related papers  (2026-05-31T09:20:53Z)
• Beyond Seeing Is Believing: On Crowdsourced Detection of Audiovisual Deepfakes [4.186575888568895]
  We study audiovisual deepfake detection by measuring how consistently crowd workers distinguish authentic from manipulated videos.<n>We run two matched crowdsourcing studies on Prolific using AV-Deepfake1M and the Trusted Media Challenge dataset.<n>Results show that crowd workers rarely misclassify authentic videos as manipulated, but they miss many manipulations, and agreement remains limited across videos.
  arXiv  Detail & Related papers  (2026-05-06T11:48:04Z)
• From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in the Wild [37.573244975096365]
  We introduce WildFakeBench, a large-scale benchmark of over 10,000 real-world micro-videos covering diverse misinformation types and sources.<n>We develop FakeAgent, a Delphi-inspired multi-agent reasoning framework that integrates multimodal understanding with external evidence.
  arXiv  Detail & Related papers  (2026-03-26T13:15:33Z)
• Video-BrowseComp: Benchmarking Agentic Video Research on Open Web [64.53060049124961]
  Video-BrowseComp is a benchmark comprising 210 questions tailored for open-web agentic video reasoning.<n>It enforces a mandatory dependency on temporal visual evidence, ensuring answers cannot be derived solely through text search.<n>As the first open-web video research benchmark, Video-BrowseComp advances the field beyond passive perception toward proactive video reasoning.
  arXiv  Detail & Related papers  (2025-12-28T19:08:27Z)
• DAVID-XR1: Detecting AI-Generated Videos with Explainable Reasoning [58.70446237944036]
  DAVID-X is the first dataset to pair AI-generated videos with detailed defect-level, temporal-spatial annotations and written rationales.<n>We present DAVID-XR1, a video-language model designed to deliver an interpretable chain of visual reasoning.<n>Our results highlight the promise of explainable detection methods for trustworthy identification of AI-generated video content.
  arXiv  Detail & Related papers  (2025-06-13T13:39:53Z)
• BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation [77.55074597806035]
  GenBuster-200K is a large-scale, high-quality AI-generated video dataset featuring 200K high-resolution video clips.<n>BusterX is a novel AI-generated video detection and explanation framework leveraging multimodal large language model (MLLM) and reinforcement learning.
  arXiv  Detail & Related papers  (2025-05-19T02:06:43Z)
• Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos [106.5804660736763]
  Video information retrieval remains a fundamental approach for accessing video content.<n>We build on the observation that retrieval models often favor AI-generated content in ad-hoc and image retrieval tasks.<n>We investigate whether similar biases emerge in the context of challenging video retrieval.
  arXiv  Detail & Related papers  (2025-02-11T07:43:47Z)
• VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs [64.60035916955837]
  VANE-Bench is a benchmark designed to assess the proficiency of Video-LMMs in detecting anomalies and inconsistencies in videos.<n>Our dataset comprises an array of videos synthetically generated using existing state-of-the-art text-to-video generation models.<n>We evaluate nine existing Video-LMMs, both open and closed sources, on this benchmarking task and find that most of the models encounter difficulties in effectively identifying the subtle anomalies.
  arXiv  Detail & Related papers  (2024-06-14T17:59:01Z)
• Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos [16.34393937800271]
  generative models in creating high-quality videos have raised concerns about digital integrity and privacy vulnerabilities. Recent works to combat Deepfakes videos have developed detectors that are highly accurate at identifying GAN-generated samples. We propose a novel framework for detecting videos synthesized from multiple state-of-the-art (SOTA) generative models.
  arXiv  Detail & Related papers  (2024-06-13T21:52:49Z)
• VGMShield: Mitigating Misuse of Video Generative Models [7.1819804607793705]
  VGMShield is a set of straightforward but effective mitigations through the lifecycle of fake video generation.<n>We start from fake video detection, trying to understand whether there is uniqueness in generated videos.<n>Then, we investigate the fake video source tracing problem, which maps a fake video back to the model that generated it.
  arXiv  Detail & Related papers  (2024-02-20T16:39:23Z)
• AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection [49.81915942821647]
  This study introduces the audio-visual transformer-based ensemble network (AVTENet) to detect deepfake videos.<n>For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset.<n>For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset.
  arXiv  Detail & Related papers  (2023-10-19T19:01:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.