Can an imitator fool a prompt-free human-vs-machine text detector with cheap surface edits, such as typos, contractions or dropping the chat preamble?
research/mimic-evade
Can cheap surface edits (typos, contractions, no chat preamble) fool the n-gram human-vs-machine detector? Most move held-out AUROC under 0.005; 2% typos cost 0.012 and keeping half the text 0.051 (from 0.927), and a refit detector wins it back.
What it found
The mimic-contexts n-gram detector (held-out AUROC 0.927) is scored on poetry, reviews and wiki after scripted edits to the machine texts only. Markdown stripping, preamble removal and contractions move it by at most 0.005; 2% typos cost 0.012 (interval −0.014 to −0.010); keeping the first half costs 0.051, the largest drop, and no edit approaches chance. A detector refit on edited training text recovers and exceeds the baseline (0.956 to 0.969), partly by learning text length. Lowercasing and whitespace edits are null by construction. Preliminary: scripted rules, not LLM-chosen edits; one seed.
A figure from its own output
Paper, code and results
More in Adversarial human imitation
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- Does refitting a human-vs-machine text detector on attacked text stop a black-box evader?Does refitting a text detector on attacked text stop a black-box evader? It settles after one refit: fresh-attack AUROC stays near 0.81 (from 0.719) while clean AUROC falls from 0.948 to about 0.90. Stylised, no fluency check.
- Does a prompt-free human-vs-machine detector carry over to a generator or decoding it never saw?Does a prompt-free detector carry over to a generator or decoding it never saw? The
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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.