International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The rapid proliferation of AI-generated content, including synthetic text, images, and multimedia, has created significant challenges in digital trust, academic integrity, and online misinformation. Traditional detection mechanisms struggle to differentiate between human-written and AI-generated content due to improvements in large language models (LLMs). This study proposes a hybrid deep learning framework for AI-generated text detection by integrating transformer-based contextual embeddings with linguistic and stylometric features. The proposed model combines RoBERTa embeddings with statistical linguistic markers and employs an ensemble classifier to improve robustness. Experimental evaluation on benchmark datasets demonstrates improved detection accuracy compared to baseline transformer-only approaches. The proposed method achieves 96.3% accuracy and shows strong generalization across unseen AI models. The results highlight the importance of hybrid modeling for reliable AI content authentication.

Keywords: AI-generated content, fake content detection, deep learning, stylometry, transformer models, misinformation detection

Downloads: | DOI: 10.17148/IJARCCE.2026.15213