Multimodal Fake News Detection System: A Comprehensive Solution Integrating ViT, BERT, and GNN

The Multi-Model-Fake-News-Detection project is a multimodal fake news detection system integrating Vision Transformer (for visual analysis), BERT/RoBERTa (for text encoding), and Graph Neural Networks (for social context modeling). It uses cross-modal attention and dynamic fusion techniques to achieve an accuracy of 89.3%, with real-time prediction and interpretability capabilities, and is open-sourced by Manognya86.

In the era of social media, the spread speed and influence of fake news have grown exponentially. Multimodal forms (text, images, etc.) make single-modal detection methods difficult to handle. This project develops a comprehensive detection system for this complex scenario.

Core Modules

1. Visual Analysis: Vision Transformer (ViT) splits image patches, captures global dependencies to identify tampered/spliced features;
2. Text Analysis: BERT/RoBERTa extract semantics to identify incendiary language and logical contradictions;
3. Social Context: Graph Neural Networks (GNN) model propagation structures to capture user interaction/forwarding paths;

Fusion Mechanism

• Cross-modal attention: dynamically assign modal weights;
• Dynamic fusion: gating mechanism adaptively adjusts fusion coefficients.

Evaluation results of the system on standard datasets:

• Text only: 82% accuracy;
• Text + Visual: 86% accuracy;
• Full multimodal: 89.3% accuracy; Real-time detection latency is in milliseconds, meeting high concurrency requirements.

Multimodal systems integrating visual, text, and social information are more accurate than single-modal ones. The open-source implementation promotes progress in the field and has significant social value in ensuring information authenticity.

1. Social Media: Real-time review and interception of fake news;
2. News Aggregation: Evaluate news credibility and label levels;
3. Public Opinion Monitoring: Track propagation trends to assist response.

Challenges

• Adversarial attack defense: handle subtle perturbations/modifications;
• Emerging fake forms: extend to video modality for deepfake detection;
• Cross-domain generalization: improve adaptability across different domains;

Directions

Optimize robustness, expand modalities, and enhance cross-domain capabilities.