The burgeoning capabilities of large language models (LLMs) have raised
growing concerns about abuse. DetectGPT, a zero-shot metric-based unsupervised
machine-generated text detector, first introduces perturbation and shows great
performance improvement. However, DetectGPT's random perturbation strategy
might introduce noise, limiting the distinguishability and further performance
improvements. Moreover, its logit regression module relies on setting the
threshold, which harms the generalizability and applicability of individual or
small-batch inputs. Hence, we propose a novel detector, \modelname{}, which
uses selective strategy perturbation to relieve the important information loss
caused by random masking, and multi-pair contrastive learning to capture the
implicit pattern information during perturbation, facilitating few-shot
performance. The experiments show that \modelname{} outperforms the SOTA method
by 1.20\% in accuracy on average on four public datasets. We further analyze
the effectiveness, robustness, and generalization of our perturbation method.

我们提出了一种新的检测器，使用选择性策略扰动减轻了随机屏蔽引起的重要信息损失，并使用多对比学习来捕捉扰动过程中的隐含模式信息，从而提高少样本学习性能。在四个公共数据集上，实验证明，我们的模型在准确度上比 SOTA 方法平均提高了 1.20%。我们进一步分析了扰动方法的有效性、稳健性和泛化性。