Authenticity Detection by Fine-Tuning Transformer-Based Models
The rapid spread of AI-generated misinformation poses serious risks to public discourse and societal stability, creating an urgent need for automated fake text detection systems. This paper presents an authenticity detection framework that integrates transformer-based architectures (RoBERTa and BERT) with parameter-efficient fine-tuning strategies, specifically DoRA and LoRA, evaluated on the FakeNewsNet dataset. The proposed RoBERTa + DoRA model achieves 88.40% accuracy on FakeNewsNet while updating less than 1% of model parameters, outperforming full fine-tuning baselines and demonstrating superior computational efficiency. Ensemble strategies further boost performance, with a hard voting ensemble reaching 92.23% accuracy. Compared to resource-intensive large language models such as ChatGPT and GPT-4, the proposed approach delivers competitive results with significantly lower computational overhead, making it suitable for real-time deployment. These findings demonstrate that modern parameter-efficient methods, when combined with modular architectures, offer a scalable and adaptable solution for fake news detection across diverse domains.