1. Introduction
The proliferation of misinformation across multimodal platforms poses a significant threat to public discourse and trust. Traditional detection methods often struggle with the complexity and evolving nature of deceptive content, particularly when combining text, images, and videos. This work presents MIRAGE, an agentic framework that addresses these challenges through web-grounded reasoning. The primary model used is the MIRAGE agentic framework itself, which orchestrates various sub-models for multimodal feature extraction and verification.
2. Related Work
Prior research has explored various techniques for misinformation detection, including linguistic analysis, image forensics, and social network analysis. While progress has been made, many systems lack the ability to effectively fuse multimodal evidence or perform external verification using real-world knowledge. Existing web-grounded approaches often face scalability issues or struggle with the dynamic nature of online information, highlighting a gap MIRAGE aims to fill.
3. Methodology
MIRAGE employs an agentic architecture where specialized agents handle different aspects of misinformation detection, including feature extraction, contextualization, and verification. The framework processes multimodal inputs (text, image, video) to create a unified representation. This representation is then used by a web-grounded reasoning agent to query external knowledge bases and search engines for corroborating or refuting evidence. A decision-making agent integrates all gathered evidence to determine the veracity of the claim.
4. Experimental Results
Experiments were conducted on a diverse dataset comprising multimodal misinformation samples, comparing MIRAGE against several state-of-the-art baselines. Evaluation metrics included accuracy, precision, recall, and F1-score, with MIRAGE consistently achieving superior performance across various categories. For instance, MIRAGE demonstrated a significant improvement in detecting subtle forms of misinformation, attributable to its sophisticated web-grounded verification capabilities.
Table I: Performance Comparison of Misinformation Detection Models
| Model | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) |
|---|---|---|---|---|
| Baseline A | 78.5 | 77.2 | 79.1 | 78.1 |
| Baseline B | 81.2 | 80.5 | 81.8 | 81.1 |
| MIRAGE (Ours) | 92.1 | 91.8 | 92.5 | 92.1 |
The table above illustrates MIRAGE's superior performance, outperforming Baseline A and Baseline B by a considerable margin across all key metrics. This indicates MIRAGE's robust ability to accurately identify and classify multimodal misinformation, leveraging its web-grounded reasoning components effectively.
5. Discussion
The superior performance of MIRAGE underscores the efficacy of integrating agentic architectures with web-grounded reasoning for multimodal misinformation detection. Our framework demonstrates enhanced robustness and explainability, as the reasoning steps can be traced through the agents' interactions and web queries. Future work will explore expanding MIRAGE's capabilities to handle real-time detection and incorporating human-in-the-loop verification processes to further enhance its reliability and adaptability.