# Latent Reconstruction from Generated Data for Multimodal Misinformation Detection

arXiv: https://arxiv.org/abs/2504.06010 · HTML: https://mlsanity.com/paper/2504.06010/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 26 of 26)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Visual News: Benchmark and Challenges in News Image Captioning](https://mlsanity.com/paper/2010.03743/) (2020)
- [Few-Shot Classification with Feature Map Reconstruction Networks](https://mlsanity.com/paper/2012.01506/) (2020)
- [NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media](https://mlsanity.com/paper/2104.05893/) (2021)
- [Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations](https://mlsanity.com/paper/2310.07849/) (2023)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2403.03170/) (2024)
- [Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources](https://mlsanity.com/paper/2112.00061/) (2021)
- [MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset](https://mlsanity.com/paper/2202.11684/) (2022)
- [MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs](https://mlsanity.com/paper/2406.08772/) (2024)
- [Multimodal Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2305.13507/) (2023)
- [Fact-Checking Meets Fauxtography: Verifying Claims About Images](https://mlsanity.com/paper/1908.11722/) (2019)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation](https://mlsanity.com/paper/2210.13865/) (2022)
- [VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Bias](https://mlsanity.com/paper/2304.14133/) (2023)
- [Multimodal Analytics for Real-world News using Measures of Cross-modal Entity Consistency](https://mlsanity.com/paper/2003.10421/) (2020)
- [Deep Multimodal Image-Repurposing Detection](https://mlsanity.com/paper/1808.06686/) (2018)
- [Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text](https://mlsanity.com/paper/1707.01606/) (2017)
- [Synthetic Misinformers: Generating and Combating Multimodal Misinformation](https://mlsanity.com/paper/2303.01217/) (2023)
- [Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation](https://mlsanity.com/paper/2311.01766/) (2023)
- [Multimodal Misinformation Detection using Large Vision-Language Models](https://mlsanity.com/paper/2407.14321/) (2024)
- [RED-DOT: Multimodal Fact-checking via Relevant Evidence Detection](https://mlsanity.com/paper/2311.09939/) (2023)
- [Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation](https://mlsanity.com/paper/2112.08594/) (2021)
- [Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?](https://mlsanity.com/paper/2407.13488/) (2024)
- [Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checking](https://mlsanity.com/paper/2404.18971/) (2024)
- [Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network](https://mlsanity.com/paper/2407.13211/) (2024)

## Built on by (most cited first, 1 of 1)

- ["Humor, Art, or Misinformation?": A Multimodal Dataset for Intent-Aware Synthetic Image Detection](https://mlsanity.com/paper/2508.20670/) (2025)

## Abstract (arXiv)

Multimodal misinformation, such as miscaptioned images, where captions misrepresent an image's origin, context, or meaning, poses a growing challenge in the digital age. Due to the scarcity of large-scale annotated datasets for multimodal misinformation detection (MMD), recent approaches rely on synthetic training data created via out-of-context pairings or named entity manipulations (e.g., altering names, dates, or locations). However, these often yield simplistic, unrealistic examples, which limits their utility as training examples. To address this, we introduce "MisCaption This!", a framework for generating high-fidelity synthetic miscaptioned datasets through Adversarial Prompting of Vision-Language Models (VLMs). Additionally, we introduce "Latent Multimodal Reconstruction" (LAMAR), a Transformer-based network trained to reconstruct the embeddings of truthful captions, providing a strong auxiliary signal to guide detection. We explore various training strategies (end-to-end vs. large-scale pre-training) and integration mechanisms (direct, mask, gate, and attention). Extensive experiments show that models trained on "MisCaption This!" data generalize better to real-world misinformation, while LAMAR achieves new state-of-the-art on NewsCLIPpings, VERITE, and the newly introduced VERITE 24/25 benchmark; highlighting the efficacy of VLM-generated data and reconstruction-based networks for advancing MMD. Our code is available at https://github.com/stevejpapad/miscaptioned-image-reconstruction