Authorship Obfuscation in Multilingual Machine-Generated Text Detection
Dominik Macko Robert Moro Adaku Uchendu Ivan Srba Jason Samuel Lucas Michiharu Yamashita Nafis Irtiza Tripto Dongwon Lee Jakub Simko Maria Bielikova
High-quality text generation capability of latest 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 detection evasion in all tested
languages, where homoglyph attacks are especially successful.