通过曲率与特征归因，实现可解释的文本生成检测。
NOTAI.AI: Explainable Detection of Machine-Generated Text via Curvature and Feature Attribution
- 融合17个可解释特征，用梯度提升树分类
- 检测准确率达98.4%，优于现有方法
- 支持实时分析与自然语言解释，适合内容审核者
我们提出NOTAI.AI，一种可解释的机器生成文本检测框架，扩展了Fast-DetectGPT，将基于曲率的信号与神经特征及文体特征结合，在监督设置下进行检测。系统整合17个可解释特征，包括条件概率曲率、ModernBERT检测得分、可读性指标和文体线索，并通过梯度提升树（XGBoost）元分类器判断文本是否由人工智能生成。此外，NOTAI.AI采用Shapley加性解释（SHAP）提供局部和全局特征归因，并通过基于大语言模型的解释层将归因结果转化为结构化自然语言理由，实现用户可理解的解释。系统部署为交互式网页应用，支持实时分析、特征可视化及结构化证据呈现。用户可通过界面输入文本，查看神经与统计信号如何影响最终决策。源代码与演示视频已公开，保障可复现性。
原文摘要 · Abstract (English)
We present NOTAI.AI, an explainable framework for machine-generated text detection that extends Fast-DetectGPT by integrating curvature-based signals with neural and stylometric features in a supervised setting. The system combines 17 interpretable features, including Conditional Probability Curvature, ModernBERT detector score, readability metrics, and stylometric cues, within a gradient-boosted tree (XGBoost) meta-classifier to determine whether a text is human- or AI-generated. Furthermore, NOTAI.AI applies Shapley Additive Explanations (SHAP) to provide both local and global feature-level attribution. These attributions are further translated into structured natural-language rationales through an LLM-based explanation layer, which enables user-facing interpretability. The system is deployed as an interactive web application that supports real-time analysis, visual feature inspection, and structured evidence presentation. A web interface allows users to input text and inspect how neural and statistical signals influence the final decision. The source code and demo video are publicly available to support reproducibility.
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