@inproceedings{al-smadi-2025-multimodal,
    title = "A Multimodal Transformer-based Approach for Cross-Domain Detection of Machine-Generated Text",
    author = "AL-Smadi, Mohammad",
    editor = "Lamsiyah, Salima  and
      Ezzini, Saad  and
      El Mahdaoui, Abdelkader  and
      Alami, Hamza  and
      Benlahbib, Abdessamad  and
      El Amrani, Samir  and
      Chafik, Salmane  and
      Hammouchi, Hicham",
    booktitle = "Proceedings of the Shared Task on Multi-Domain Detection of AI-Generated Text",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-mdaigt.4/",
    pages = "20--25",
    abstract = "The rapid advancement of large language models (LLMs) has made it increasingly challenging to distinguish between human-written and machine-generated content. This paper presents IntegrityAI, a multimodal ELECTRA-based model for the detection of AI-generated text across multiple domains. Our approach combines textual features processed through a pre-trained ELECTRA model with handcrafted stylometric features to create a robust classifier. We evaluate our system on the Multi-Domain Detection of AI-Generated Text (M-DAIGT) shared task, which focuses on identifying AI-generated content in news articles and academic writing. IntegrityAI achieves exceptional performance and ranked 1st in both subtasks, with F1-scores of 99.6{\%} and 99.9{\%} on the news article detection and academic writing detection subtasks, respectively. Our results demonstrate the effectiveness of combining transformer-based models with stylometric analysis for detecting AI-generated content across diverse domains and writing styles."
}