M Ganesh Karthik, Faizz Ahmad Khadri Syed, Pamidimukkala Sai Geetha, Sathe Asha Prashant, G N V G Sirisha, M Sitha Ram, Ch Koteswararao
Department of Computer Science and Engineering, GITAM School of Technology, GITAM University- Bengaluru Campus, Bengaluru, India.
Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, India.
Sci Rep. 2025 Apr 28;15(1):14782. doi: 10.1038/s41598-025-99936-3.
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.
结合文本和图像的多模态虚假新闻的大规模生产，由于分布差异，带来了重大的检测挑战。传统检测器在开放世界场景中面临困难，而大型视觉语言模型（LVLMs）在识别局部伪造方面缺乏特异性。现有方法往往高估了公众舆论的影响，未能在早期阶段遏制错误信息。本研究引入了一种改进的Transformer（MT）模型，使用伪造新闻文章分三个阶段进行微调。该模型使用PSODO（一种粒子群优化和蒲公英优化的混合算法）进一步优化，解决了收敛速度慢和陷入局部最优等局限性。PSODO通过整合全局和局部搜索策略提高了搜索效率。在基准数据集上的实验结果表明，所提出的方法显著提高了虚假新闻检测的准确性。该模型有效地捕捉了分布不一致性和多模态伪造细节，优于传统检测器和LVLMs。本研究强调了整合Transformer和混合优化以开发通用、可扩展和准确的虚假新闻检测系统的重要性。
