# Exploiting Multi-domain Visual Information for Fake News Detection

arXiv: https://arxiv.org/abs/1908.04472 · HTML: https://mlsanity.com/paper/1908.04472/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 30 of 48)

- [SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2403.03170/) (2024)
- [Multimodal Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2305.13507/) (2023)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Zoom Out and Observe: News Environment Perception for Fake News Detection](https://mlsanity.com/paper/2203.10885/) (2022)
- [Towards Fine-Grained Reasoning for Fake News Detection](https://mlsanity.com/paper/2110.15064/) (2021)
- [Bootstrapping Multi-view Representations for Fake News Detection](https://mlsanity.com/paper/2206.05741/) (2022)
- [Cross-modal Contrastive Learning for Multimodal Fake News Detection](https://mlsanity.com/paper/2302.14057/) (2023)
- [Multimodal Emergent Fake News Detection via Meta Neural Process Networks](https://mlsanity.com/paper/2106.13711/) (2021)
- [Diffusion Noise Feature: Accurate and Fast Generated Image Detection](https://mlsanity.com/paper/2312.02625/) (2023)
- [A Survey on Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2103.12506/) (2021)
- [Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection](https://mlsanity.com/paper/2306.14728/) (2023)
- [Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2112.10322/) (2021)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [Multi-modal Fake News Detection on Social Media via Multi-grained Information Fusion](https://mlsanity.com/paper/2304.00827/) (2023)
- [MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models](https://mlsanity.com/paper/2406.11288/) (2024)
- [Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection](https://mlsanity.com/paper/2306.02137/) (2023)
- [Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting](https://mlsanity.com/paper/2310.10429/) (2023)
- [Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection](https://mlsanity.com/paper/2412.14686/) (2024)
- [Harmfully Manipulated Images Matter in Multimodal Misinformation Detection](https://mlsanity.com/paper/2407.19192/) (2024)
- [GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection](https://mlsanity.com/paper/2202.12478/) (2022)
- [Team Yao at Factify 2022: Utilizing Pre-trained Models and Co-attention Networks for Multi-Modal Fact Verification](https://mlsanity.com/paper/2201.11664/) (2022)
- [GREENER: Graph Neural Networks for News Media Profiling](https://mlsanity.com/paper/2211.05533/) (2022)
- [TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection](https://mlsanity.com/paper/2304.09421/) (2023)
- [Fake News Detection with Heterogeneous Transformer](https://mlsanity.com/paper/2205.03100/) (2022)
- [Traceable and Authenticable Image Tagging for Fake News Detection](https://mlsanity.com/paper/2211.10923/) (2022)
- [KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection](https://mlsanity.com/paper/2507.09647/) (2025)
- [INO at Factify 2: Structure Coherence based Multi-Modal Fact Verification](https://mlsanity.com/paper/2303.01510/) (2023)
- [VGA: Vision and Graph Fused Attention Network for Rumor Detection](https://mlsanity.com/paper/2401.01759/) (2024)
- [FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections](https://mlsanity.com/paper/2403.09858/) (2024)
- [Search, Examine and Early-Termination: Fake News Detection with Annotation-Free Evidences](https://mlsanity.com/paper/2407.07931/) (2024)

## Abstract (arXiv)

The increasing popularity of social media promotes the proliferation of fake news. With the development of multimedia technology, fake news attempts to utilize multimedia contents with images or videos to attract and mislead readers for rapid dissemination, which makes visual contents an important part of fake news. Fake-news images, images attached in fake news posts,include not only fake images which are maliciously tampered but also real images which are wrongly used to represent irrelevant events. Hence, how to fully exploit the inherent characteristics of fake-news images is an important but challenging problem for fake news detection. In the real world, fake-news images may have significantly different characteristics from real-news images at both physical and semantic levels, which can be clearly reflected in the frequency and pixel domain, respectively. Therefore, we propose a novel framework Multi-domain Visual Neural Network (MVNN) to fuse the visual information of frequency and pixel domains for detecting fake news. Specifically, we design a CNN-based network to automatically capture the complex patterns of fake-news images in the frequency domain; and utilize a multi-branch CNN-RNN model to extract visual features from different semantic levels in the pixel domain. An attention mechanism is utilized to fuse the feature representations of frequency and pixel domains dynamically. Extensive experiments conducted on a real-world dataset demonstrate that MVNN outperforms existing methods with at least 9.2% in accuracy, and can help improve the performance of multimodal fake news detection by over 5.2%.