  Machine learning based language models have recently made significant
progress, which introduces a danger to spread misinformation. To combat this
potential danger, several methods have been proposed for detecting text written
by these language models. This paper presents two classes of black-box attacks
on these detectors, one which randomly replaces characters with homoglyphs, and
the other a simple scheme to purposefully misspell words. The homoglyph and
misspelling attacks decrease a popular neural text detector's recall on neural
text from 97.44% to 0.26% and 22.68%, respectively. Results also indicate that
the attacks are transferable to other neural text detectors.
