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arXiv Machine Learning By Sina Mansouri, Mohit Marvania, Abolfazl Safikhani

Stability-Aware Feature Design for Robust Watermark Detection in Machine-Generated Text

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The paper introduces Pattern Stability Score (PSS), a watermark detection framework that uses local statistical features and stability dynamics across paraphrased variants to identify machine-generated text. PSS combines global and local z‑score features with higher‑order run‑length statistics, autocorrelation signals, and stability scores over paraphrase depth. Experiments on PG‑19, CNN/DailyMail, and WikiText with Llama‑3‑8B, Qwen2‑7B, and multiple paraphrasers show that PSS improves detection AUC by 10‑15 percentage points and a single universal classifier achieves over 87.8% AUC across diverse LLMs, paraphrasers, and domains without retraining.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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