Random at GenAI Detection Task 3: A Hybrid Approach to Cross-Domain Detection of Machine-Generated Text with Adversarial Attack Mitigation

Shifali Agrahari, Prabhat Mishra, Sujit Kumar


Abstract
Machine-generated text (MGT) detection has gained critical importance in the era of large language models, especially for maintaining trust in multilingual and cross-domain applica- tions. This paper presents Task 3 Subtask B: Adversarial Cross-Domain MGT Detection for in the COLING 2025 DAIGenC Workshop. Task 3 emphasizes the complexity of detecting AI-generated text across eight domains, eleven generative models, and four decoding strate- gies, with an added challenge of adversarial manipulation. We propose a robust detection framework transformer embeddings utilizing Domain-Adversarial Neural Networks (DANN) to address domain variability and adversarial robustness. Our model demonstrates strong performance in identifying AI-generated text under adversarial conditions while highlighting condition scope of future improvement.
Anthology ID:
2025.genaidetect-1.43
Volume:
Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)
Month:
January
Year:
2025
Address:
Abu Dhabi, UAE
Editors:
Firoj Alam, Preslav Nakov, Nizar Habash, Iryna Gurevych, Shammur Chowdhury, Artem Shelmanov, Yuxia Wang, Ekaterina Artemova, Mucahid Kutlu, George Mikros
Venues:
GenAIDetect | WS
SIG:
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
365–370
Language:
URL:
https://aclanthology.org/2025.genaidetect-1.43/
DOI:
Bibkey:
Cite (ACL):
Shifali Agrahari, Prabhat Mishra, and Sujit Kumar. 2025. Random at GenAI Detection Task 3: A Hybrid Approach to Cross-Domain Detection of Machine-Generated Text with Adversarial Attack Mitigation. In Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect), pages 365–370, Abu Dhabi, UAE. International Conference on Computational Linguistics.
Cite (Informal):
Random at GenAI Detection Task 3: A Hybrid Approach to Cross-Domain Detection of Machine-Generated Text with Adversarial Attack Mitigation (Agrahari et al., GenAIDetect 2025)
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PDF:
https://aclanthology.org/2025.genaidetect-1.43.pdf