Neural text detectors are models trained to detect whether a given text was
generated by a language model or written by a human. In this paper, we
investigate three simple and resource-efficient strategies (parameter tweaking,
prompt engineering, and character-level mutations) to alter texts generated by
GPT-3.5 that are unsuspicious or unnoticeable for humans but cause
misclassification by neural text detectors. The results show that especially
parameter tweaking and character-level mutations are effective strategies.

通过调整参数、优化提示语和进行字符级别的变异，我们研究了改变 GPT-3.5 生成的文本的三种简单且资源高效的策略，这些策略在人类不会察觉但能使神经文本检测器误分类的文本中特别有效。

神经文本检测器的高效黑盒对抗攻击

Efficient Black-Box Adversarial Attacks on Neural Text Detectors

Neural text detectors aim to decide the characteristics that distinguish
neural (machine-generated) from human texts. To challenge such detectors,
adversarial attacks can alter the statistical characteristics of the generated
text, making the detection task more and more difficult. Inspired by the
advances of mutation analysis in software development and testing, in this
paper, we propose character- and word-based mutation operators for generating
adversarial samples to attack state-of-the-art natural text detectors. This
falls under white-box adversarial attacks. In such attacks, attackers have
access to the original text and create mutation instances based on this
original text. The ultimate goal is to confuse machine learning models and
classifiers and decrease their prediction accuracy.

本论文提出了基于字符和词语的变异操作方法，用于生成对抗样本以攻击最先进的自然文本检测器，从而逐渐减少机器学习模型和分类器的预测准确性。