@inproceedings{shportko-verbitsky-2025-paraphrasing,
title = "Paraphrasing Attack Resilience of Various Machine-Generated Text Detection Methods",
author = "Shportko, Andrii and
Verbitsky, Inessa",
editor = "Ebrahimi, Abteen and
Haider, Samar and
Liu, Emmy and
Haider, Sammar and
Leonor Pacheco, Maria and
Wein, Shira",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
month = apr,
year = "2025",
address = "Albuquerque, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-srw.46/",
doi = "10.18653/v1/2025.naacl-srw.46",
pages = "474--484",
ISBN = "979-8-89176-192-6",
abstract = "The recent large-scale emergence of LLMs has left an open space for dealing with their consequences, such as plagiarism or the spread of false information on the Internet. Coupling this with the rise of AI detector bypassing tools, reliable machine-generated text detection is in increasingly high demand. We investigate the paraphrasing attack resilience of various machine-generated text detection methods, evaluating three approaches: fine-tuned RoBERTa, Binoculars, and text feature analysis, along with their ensembles using Random Forest classifiers. We discovered that Binoculars-inclusive ensembles yield the strongest results, but they also suffer the most significant losses during attacks. In this paper, we present the dichotomy of performance versus resilience in the world of AI text detection, which complicates the current perception of reliability among state-of-the-art techniques."
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%0 Conference Proceedings
%T Paraphrasing Attack Resilience of Various Machine-Generated Text Detection Methods
%A Shportko, Andrii
%A Verbitsky, Inessa
%Y Ebrahimi, Abteen
%Y Haider, Samar
%Y Liu, Emmy
%Y Haider, Sammar
%Y Leonor Pacheco, Maria
%Y Wein, Shira
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, USA
%@ 979-8-89176-192-6
%F shportko-verbitsky-2025-paraphrasing
%X The recent large-scale emergence of LLMs has left an open space for dealing with their consequences, such as plagiarism or the spread of false information on the Internet. Coupling this with the rise of AI detector bypassing tools, reliable machine-generated text detection is in increasingly high demand. We investigate the paraphrasing attack resilience of various machine-generated text detection methods, evaluating three approaches: fine-tuned RoBERTa, Binoculars, and text feature analysis, along with their ensembles using Random Forest classifiers. We discovered that Binoculars-inclusive ensembles yield the strongest results, but they also suffer the most significant losses during attacks. In this paper, we present the dichotomy of performance versus resilience in the world of AI text detection, which complicates the current perception of reliability among state-of-the-art techniques.
%R 10.18653/v1/2025.naacl-srw.46
%U https://aclanthology.org/2025.naacl-srw.46/
%U https://doi.org/10.18653/v1/2025.naacl-srw.46
%P 474-484
Markdown (Informal)
[Paraphrasing Attack Resilience of Various Machine-Generated Text Detection Methods](https://aclanthology.org/2025.naacl-srw.46/) (Shportko & Verbitsky, NAACL 2025)
ACL