Attacks on Machine-Text Detectors Retain Stylistic Fingerprints

The paper investigates the effectiveness of current evasion strategies against machine-text detectors, revealing that while techniques like prompt engineering can degrade detector performance, they do not eliminate the stylistic fingerprints of machine-generated text. The authors introduce a novel paraphrasing approach that successfully evades detection across various detectors, including those leveraging stylistic features. Their findings suggest that reliable machine-text detection may necessitate a shift from single-document to multi-document analysis to maintain efficacy against evolving evasion methods.