Hybrid optimization driven fake news detection using reinforced transformer models

The large-scale production of multimodal fake news, combining text and images, presents significant detection challenges due to distribution discrepancies. Traditional detectors struggle with open-world scenarios, while Large Vision-Language Models (LVLMs) lack specificity in identifying local forgeries. Existing methods often overestimate public opinion's impact, failing to curb misinformation at early stages. This study introduces a Modified Transformer (MT) model, fine-tuned in three stages using fabricated news articles. The model is further optimized using PSODO, a hybrid Particle Swarm Optimization and Dandelion Optimization algorithm, addressing limitations such as slow convergence and local optima entrapment. PSODO enhances search efficiency by integrating global and local search strategies. Experimental results on benchmark datasets demonstrate that the proposed approach significantly improves fake news detection accuracy. The model effectively captures distribution inconsistencies and multimodal forgery details, outperforming conventional detectors and LVLMs. This research highlights the importance of integrating transformers and hybrid optimization to develop generalized, scalable, and accurate fake news detection systems.