提出三维检测框架，提升文本生成内容识别的抗攻击能力。
Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks
- 从内容与表达两方面补充统计检测视角
- 在17种攻击下，准确率提升22.3%（AUROC）
- 适合需要高可靠性的内容安全场景
现有AI生成文本检测方法易受操控文本特征的攻击。本文提出三重视角检测框架Triospect，通过引入文本的核心思想（内容）和风格特征（表达）两个额外维度，在包含17种攻击、12个领域和17个源模型的两个基准上进行实验。结果表明，Triospect对各类攻击具有强鲁棒性：在Humanize-16K后攻击子集上，相比强基线，AUROC提升22.3%，TPR01提升13%；在对抗性RAID数据集上，AUROC提升9.1%，TPR01提升22%。该框架是统计方法在提升检测可靠性方面的开创性尝试。代码与数据已开源：https://github.com/baoguangsheng/triospect。
原文摘要 · Abstract (English)
Existing AI-generated text detectors are vulnerable to attacks that manipulate textual characteristics. In this study, we propose a novel Triospect Detection Framework by using additional perspectives of content (core ideas) and expression (stylistic elements) within a given text. Experiments on two benchmarks involving 17 attacks, 12 domains, and 17 source models demonstrate that Triospect is robust against these attacks. It improves the strong baseline by a significant margin of 22.3% (AUROC) and 13% (TPR01) on the Humanize-16K after-attack subset, and by 9.1% (AUROC) and 22% (TPR01) on the adversarial RAID. This framework marks a pioneering effort in statistical methods to enhance detection reliability against attacks. We release our data and code at https://github.com/baoguangsheng/triospect.
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