Enhancing the Robustness of AI-Generated Text Detectors: A Survey

In recent years, AI-generated text (AIGT) detection has attracted increasing attention, and some detectors demonstrate high accuracy in benchmark settings. However, the complexity and diversity of AIGT and counter-detection methods in real-world applications present substantial challenges for AIGT detection. Consequently, there is a growing demand for more robust AIGT detectors. This survey provides a systematic overview of existing research on enhancing the robustness of AIGT detectors. We categorize the focus of related literature into three key areas: text perturbation robustness, out-of-distribution (OOD) robustness, and AI–human hybrid text (AHT) detection robustness. For each area, we thoroughly summarize and analyze the corresponding robustness enhancement methods and additionally incorporate some approaches from other fields as a supplement. We also methodically organize relevant benchmark datasets, robustness evaluation methods, and metrics used to assess detectors’ performance. Then, through experiments, we evaluate the robustness of several commonly used detectors. Experiments show that text perturbations, OOD text, and AHT all affect the performance of these detectors, revealing that there remains significant room for improvement in their robustness. Finally, we suggest promising future directions based on the current issues faced by AIGT detectors and the detection requirements in real-world scenarios. To the best of our knowledge, this is the first review focused specifically on the robustness of AIGT detection.