当个性化技巧欺骗检测器：机器生成文本检测中的特征反向陷阱
When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detection
AI总结：
本文提出了一种检测器在个性化文本中性能变化的预测方法，揭示了特征反向陷阱对检测器鲁棒性的影响，并通过实验验证了该方法的有效性。
摘要：
大型语言模型（LLMs）在语言生成方面能力增强，能够生成流畅文本并模仿个人风格。然而，这种能力也增加了身份冒充的风险。本文首次提出一个基准测试集，用于评估检测器在个性化设置下的鲁棒性，该测试集由文学和博客文本及其LLM生成的模仿文本组成。实验结果表明，在个性化设置下，检测器性能存在显著差异：一些最先进的模型在个性化文本中表现下降明显。我们发现这种限制源于特征反向陷阱，即在通用领域中具有判别性的特征在个性化文本中变得误导性。基于此发现，我们提出了一种简单可靠的方法来预测检测器在个性化设置下的性能变化。该方法识别出与反向特征对应的潜在方向，并构建了主要沿这些特征变化的探测数据集来评估检测器的依赖性。实验表明，该方法能够准确预测性能变化的方向和幅度，与实际性能差距的85%相关性。我们希望这项工作能推动个性化文本检测的进一步研究。
英文摘要：
Large language models (LLMs) have grown more powerful in language generation, producing fluent text and even imitating personal style. Yet, this ability also heightens the risk of identity impersonation. To the best of our knowledge, no prior work has examined personalized machine-generated text (MGT) detection. In this paper, we introduce \dataset, the first benchmark for evaluating detector robustness in personalized settings, built from literary and blog texts paired with their LLM-generated imitations. Our experimental results demonstrate large performance gaps across detectors in personalized settings: some state-of-the-art models suffer significant drops. We attribute this limitation to the \textit{feature-inversion trap}, where features that are discriminative in general domains become inverted and misleading when applied to personalized text. Based on this finding, we propose \method, a simple and reliable way to predict detector performance changes in personalized settings. \method identifies latent directions corresponding to inverted features and constructs probe datasets that differ primarily along these features to evaluate detector dependence. Our experiments show that \method can accurately predict both the direction and the magnitude of post-transfer changes, showing 85\% correlation with the actual performance gaps. We hope that this work will encourage further research on personalized text detection.
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