Adversarial conditions such as paraphrasing and targeted style transfer sharply degrade the accuracy of machine text detectors. A document, however, carries multiple complementary signals (e.g., stylistic features, likelihood and rank-order features, and structural features), and an attack that suppresses one may leave others intact. While a parametric classifier can learn to combine these features given sufficient supervision, classifiers are prone to making confidently incorrect predictions when the distribution shifts (e.g., novel attacks or unseen language models). To address this, we propose a multi-view, non-parametric detection framework that extracts complementary feature views from the same document and aggregates per-view evidence through a Gaussian process ensemble. By aggregating evidence across views, an adversary must simultaneously defeat multiple independent axes of detection, substantially raising the cost of evasion. The Gaussian process formulation additionally provides calibrated probabilities and principled abstention on out-of-distribution inputs, supporting reliable deployment in high-stakes settings. We evaluate on three benchmarks spanning diverse generators and attacks: the DetectRL and RAID benchmarks, and the PAN2025 shared task and demonstrate that our multi-view detector maintains strong performance under the considered attacks, outperforming existing approaches against held out attacks.

翻译：对抗性条件（如释义和定向风格迁移）会显著降低机器文本检测器的准确率。然而，一篇文章携带多种互补信号（例如风格特征、似然与排序特征以及结构特征），针对某一信号的攻击可能对其余信号无效。尽管参数化分类器在充足监督下可学习组合这些特征，但当数据分布发生偏移（如新型攻击或未见语言模型）时，分类器极易做出过度自信的错误预测。为解决此问题，我们提出了一种多视图、非参数化检测框架，该框架从同一文档中提取互补特征视图，并通过高斯过程集成聚合各视图证据。通过跨视图证据聚合，攻击者必须同时击败多个独立的检测维度，从而显著提高规避成本。高斯过程公式还提供校准概率和对分布外输入的原则性弃权机制，支持在高风险场景中可靠部署。我们在涵盖多种生成器和攻击的三个基准（DetectRL、RAID基准及PAN2025共享任务）上进行了评估，结果表明，我们提出的多视图检测器在目标攻击下保持强劲性能，并在应对未见攻击方面优于现有方法。