The generation of text that is increasingly human-like has been enabled by
the advent of large language models (LLMs). As the detection of AI-generated
content holds significant importance in the fight against issues such as
misinformation and academic cheating, numerous studies have been conducted to
develop reliable LLM detectors. While promising results have been demonstrated
by such detectors on test data, recent research has revealed that they can be
circumvented by employing different techniques. In this article,
homoglyph-based ($a \rightarrow {\alpha}$) attacks that can be used to
circumvent existing LLM detectors are presented. The efficacy of the attacks is
illustrated by analizing how homoglyphs shift the tokenization of the text, and
thus its token loglikelihoods. A comprehensive evaluation is conducted to
assess the effectiveness of homoglyphs on state-of-the-art LLM detectors,
including Binoculars, DetectGPT, OpenAI's detector, and watermarking
techniques, on five different datasets. A significant reduction in the
efficiency of all the studied configurations of detectors and datasets, down to
an accuracy of 0.5 (random guessing), is demonstrated by the proposed approach.
The results show that homoglyph-based attacks can effectively evade existing
LLM detectors, and the implications of these findings are discussed along with
possible defenses against such attacks.

本研究通过分析同形异义字如何转移文本的记号化和标记的对数似然值，对比了现有大语言模型检测器在五个不同数据集上与同形异义字攻击的有效性，发现同形异义字攻击可以有效躲避现有的大语言模型检测器， 讨论了这些发现的影响以及可能的防御方法。