Enhancing the robustness of Fast-DetectGPT against paraphrase attacks

Large Language Models (LLMs) have been able to generate high-quality text and demonstrate excellent performance in various tasks. However, the ability of large language models to generate increasingly efficient human like texts has also raised concerns about their misuse for malicious purposes. Therefore, it is urgent to design a reliable and robust method for generating text detection. The zero-shot detector Fast-DetectGPT not only demonstrates strong performance in detection accuracy, but is also commendable in detection speed. However, it is unclear whether it can still maintain such a high detection success rate in the face of paraphrase attacks. In this article, we use various paraphrase attacks to perturb text and evaluate the robustness of Fast-DetectGPT, and find a significant decrease in its performance. Meanwhile, we propose Sentence-DetectGPT to optimize the detection process of Fast-DetectGPT, greatly enhancing its robustness in the face of paraphrase attacks.