Authorship Obfuscation in Multilingual Machine-Generated Text Detection

• URL: http://arxiv.org/abs/2401.07867v3
• Date: Fri, 04 Oct 2024 07:45:22 GMT
• Title: Authorship Obfuscation in Multilingual Machine-Generated Text Detection
• Authors: Dominik Macko, Robert Moro, Adaku Uchendu, Ivan Srba, Jason Samuel Lucas, Michiharu Yamashita, Nafis Irtiza Tripto, Dongwon Lee, Jakub Simko, Maria Bielikova,
• Abstract summary: Authorship obfuscation (AO) methods can cause machine-generated text (MGT) detection to evade detection. We benchmark 10 well-known AO methods against 37 MGT detection methods against MGTs in 11 languages. The results indicate that all tested AO methods can cause evasion of automated detection in all tested languages, where homoglyph attacks are especially successful.
• Score: 5.658790347990285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-quality text generation capability of recent Large Language Models (LLMs) causes concerns about their misuse (e.g., in massive generation/spread of disinformation). Machine-generated text (MGT) detection is important to cope with such threats. However, it is susceptible to authorship obfuscation (AO) methods, such as paraphrasing, which can cause MGTs to evade detection. So far, this was evaluated only in monolingual settings. Thus, the susceptibility of recently proposed multilingual detectors is still unknown. We fill this gap by comprehensively benchmarking the performance of 10 well-known AO methods, attacking 37 MGT detection methods against MGTs in 11 languages (i.e., 10 $\times$ 37 $\times$ 11 = 4,070 combinations). We also evaluate the effect of data augmentation on adversarial robustness using obfuscated texts. The results indicate that all tested AO methods can cause evasion of automated detection in all tested languages, where homoglyph attacks are especially successful. However, some of the AO methods severely damaged the text, making it no longer readable or easily recognizable by humans (e.g., changed language, weird characters).

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