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Hybrid optimization driven fake news detection using reinforced transformer models.

Ganesh Karthik M1, Khadri Syed Faizz Ahmad2, Sai Geetha Pamidimukkala3

  • 1Department of Computer Science and Engineering, GITAM School of Technology, GITAM University- Bengaluru Campus, Bengaluru, India.

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|April 28, 2025
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Summary

This study presents an advanced Modified Transformer (MT) model for detecting multimodal fake news, improving accuracy by integrating hybrid optimization. The novel approach effectively identifies sophisticated forgeries, enhancing misinformation detection systems.

Keywords:
Fake news detectionHybrid optimizationLarge Vision-Language modelsModified transformerPublic opinion analysis

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Information Security

Background:

  • Multimodal fake news (text and images) poses detection challenges due to distribution discrepancies.
  • Existing detectors and Large Vision-Language Models (LVLMs) have limitations in open-world scenarios and identifying local forgeries.
  • Current methods often fail to curb misinformation effectively at early stages.

Purpose of the Study:

  • To develop a robust and accurate system for detecting large-scale multimodal fake news.
  • To address the limitations of traditional detectors and LVLMs in identifying sophisticated forgeries.
  • To improve the efficiency and effectiveness of fake news detection by optimizing model performance.

Main Methods:

  • Introduction of a Modified Transformer (MT) model, fine-tuned in three stages on fabricated news articles.
  • Optimization of the MT model using PSODO, a hybrid Particle Swarm Optimization and Dandelion Optimization algorithm.
  • Integration of global and local search strategies within PSODO to enhance search efficiency and overcome local optima.

Main Results:

  • The proposed approach significantly improves fake news detection accuracy on benchmark datasets.
  • The MT model effectively captures distribution inconsistencies and multimodal forgery details.
  • Experimental results demonstrate superior performance compared to conventional detectors and LVLMs.

Conclusions:

  • The integration of transformers and hybrid optimization is crucial for developing generalized, scalable, and accurate fake news detection systems.
  • The proposed method offers a promising solution for combating the spread of sophisticated multimodal fake news.
  • This research contributes to advancing the field of misinformation detection with a more effective and efficient approach.