@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T A Multimodal Transformer-based Approach for Cross-Domain Detection of Machine-Generated Text
%A AL-Smadi, Mohammad
%Y Lamsiyah, Salima
%Y Ezzini, Saad
%Y El Mahdaoui, Abdelkader
%Y Alami, Hamza
%Y Benlahbib, Abdessamad
%Y El Amrani, Samir
%Y Chafik, Salmane
%Y Hammouchi, Hicham
%S Proceedings of the Shared Task on Multi-Domain Detection of AI-Generated Text
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F al-smadi-2025-multimodal
%X 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.
%U https://aclanthology.org/2025.ranlp-mdaigt.4/
%P 20-25
Markdown (Informal)
[A Multimodal Transformer-based Approach for Cross-Domain Detection of Machine-Generated Text](https://aclanthology.org/2025.ranlp-mdaigt.4/) (AL-Smadi, RANLP 2025)
ACL