StackTrace-AI: Identifying Generative AI Text Origins using Ensemble Learning

Generative AI models capable of producing human-like text, images, and other content are rapidly transforming the digital landscape. As their use becomes increasingly widespread across domains such as education, media, and communication, their outputs are shaping public discourse, influencing opinions, and even spreading misinformation. In this context, accurately identifying the source of AI-generated text is becoming increasingly crucial for ensuring transparency and accountability, protecting information integrity, mitigating deception, and fostering public trust in digital content. This study presents a source attribution framework designed to distinguish between texts generated by two leading large language models: ChatGPT and Gemini. A dataset of 2,786 AI-generated samples was constructed for this purpose. We employed transformer-based feature extraction techniques, including BERT, ALBERT, and RoBERTa, as well as a Hybrid model (AR-BERT, which combines ALBERT and RoBERTa), to capture distinctive linguistic signatures. This was followed by classification using nine traditional and ensemble machine learning algorithms. Among these, the AR-BERT-based features combined with a stacking ensemble classifier, StackTrace-AI, achieved the highest accuracy of 84.44%, surpassing all other combinations. The findings underscore the viability of transformer-driven source detection and offer valuable insights for advancing AI accountability, generative content auditing, and responsible AI governance.