# Multimodal Misinformation Detection using Large Vision-Language Models

arXiv: https://arxiv.org/abs/2407.14321 · HTML: https://mlsanity.com/paper/2407.14321/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in MLLM jailbreaks and adversarial robustness, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 22 of its 57 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM jailbreaks and adversarial robustness](https://mlsanity.com/mllm/mllm-jailbreaks-and-adversarial-robustness/)

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension](https://mlsanity.com/paper/2307.16125/) (2023)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations](https://mlsanity.com/paper/2301.04246/) (2023)
- [End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models](https://mlsanity.com/paper/2205.12487/) (2022)
- [SEED-Bench-2: Benchmarking Multimodal Large Language Models](https://mlsanity.com/paper/2311.17092/) (2023)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [Fact-Checking Meets Fauxtography: Verifying Claims About Images](https://mlsanity.com/paper/1908.11722/) (2019)
- [Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News](https://mlsanity.com/paper/2009.07698/) (2020)
- [FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking](https://mlsanity.com/paper/2309.00240/) (2023)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [Improving Generalization for Multimodal Fake News Detection](https://mlsanity.com/paper/2305.18599/) (2023)

## Built on by (most cited first, 21 of 21)

- [DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts](https://mlsanity.com/paper/2412.10510/) (2024)
- [Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies](https://mlsanity.com/paper/2503.00724/) (2025)
- [Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts](https://mlsanity.com/paper/2502.17297/) (2025)
- [Robust Fake News Detection using Large Language Models under Adversarial Sentiment Attacks](https://mlsanity.com/paper/2601.15277/) (2026)
- [E2LVLM:Evidence-Enhanced Large Vision-Language Model for Multimodal Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2502.10455/) (2025)
- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [Verifying Cross-modal Entity Consistency in News using Vision-language Models](https://mlsanity.com/paper/2501.11403/) (2025)
- [T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search](https://mlsanity.com/paper/2505.19768/) (2025)
- [Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2509.25991/) (2025)
- [From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in the Wild](https://mlsanity.com/paper/2603.25423/) (2026)
- [DECEIVE-AFC: Adversarial Claim Attacks against Search-Enabled LLM-based Fact-Checking Systems](https://mlsanity.com/paper/2602.02569/) (2026)
- [Latent Reconstruction from Generated Data for Multimodal Misinformation Detection](https://mlsanity.com/paper/2504.06010/) (2025)
- [Unveiling Modality Bias: Automated Sample-Specific Analysis for Multimodal Misinformation Benchmarks](https://mlsanity.com/paper/2511.05883/) (2025)
- [A parameter-efficient three-branch architecture for multimodal misinformation detection with limited annotations](https://mlsanity.com/paper/2512.20257/) (2025)
- [Fact or Fake? Assessing the Role of Deepfake Detectors in Multimodal Misinformation Detection](https://mlsanity.com/paper/2602.01854/) (2026)
- [M2-Verify: A Large-Scale Multidomain Benchmark for Checking Multimodal Claim Consistency](https://mlsanity.com/paper/2604.01306/) (2026)
- [KGAlign: Joint Semantic-Structural Knowledge Encoding for Multimodal Fake News Detection](https://mlsanity.com/paper/2505.14714/) (2025)
- [Towards Explainable Bilingual Multimodal Misinformation Detection and Localization](https://mlsanity.com/paper/2506.22930/) (2025)
- [DANCE: Doubly Adaptive Neighborhood Conformal Estimation](https://mlsanity.com/paper/2602.20652/) (2026)
- [Evidence Triangulation for Multimodal Fact-Checking in the Wild](https://mlsanity.com/paper/2606.31367/) (2026)
- [NepOOC-M: Bilingual Nepali-English Benchmark and Comparative Analysis of Multimodal Architectures for OOC Detection](https://mlsanity.com/paper/2608.19212/) (2026)

## Abstract (arXiv)

The increasing proliferation of misinformation and its alarming impact have motivated both industry and academia to develop approaches for misinformation detection and fact checking. Recent advances on large language models (LLMs) have shown remarkable performance in various tasks, but whether and how LLMs could help with misinformation detection remains relatively underexplored. Most of existing state-of-the-art approaches either do not consider evidence and solely focus on claim related features or assume the evidence to be provided. Few approaches consider evidence retrieval as part of the misinformation detection but rely on fine-tuning models. In this paper, we investigate the potential of LLMs for misinformation detection in a zero-shot setting. We incorporate an evidence retrieval component into the process as it is crucial to gather pertinent information from various sources to detect the veracity of claims. To this end, we propose a novel re-ranking approach for multimodal evidence retrieval using both LLMs and large vision-language models (LVLM). The retrieved evidence samples (images and texts) serve as the input for an LVLM-based approach for multimodal fact verification (LVLM4FV). To enable a fair evaluation, we address the issue of incomplete ground truth for evidence samples in an existing evidence retrieval dataset by annotating a more complete set of evidence samples for both image and text retrieval. Our experimental results on two datasets demonstrate the superiority of the proposed approach in both evidence retrieval and fact verification tasks and also better generalization capability across dataset compared to the supervised baseline.