Attacks on Machine-Text Detectors Retain Stylistic Fingerprints 文章

ArXiv CS.CL2026-06-10NEWSen作者: Rafael Rivera Soto, Barry Chen, Nicholas Andrews

详细信息

来源站点
ArXiv CS.CL
作者
Rafael Rivera Soto, Barry Chen, Nicholas Andrews
文章类型
NEWS
语言
en
发布日期
2026-06-10

摘要

arXiv:2505.14608v3 Announce Type: replace Abstract: Despite considerable progress in the development of machine-text detectors, the ease with which machine-text can be manipulated to evade detection has led to suggestions that the problem is inherently intractable. In this work, we investigate the limits of such evasion strategies. We demonstrate that while current attacks, ranging from prompt engineering to detector-guided optimization can effectively degrade performance of standard detectors, they fail to erase the underlying stylistic "fingerprints" of machine text. We show that few-shot detectors that utilize the stylistic feature space are robust to these evasion attempts, reliably detecting samples even from models explicitly tuned to prevent detection. This raises the question: does style represent a universal defense against machine-detection attacks?

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