  As LLMs become commonplace, machine-generated text has the potential to flood
the internet with spam, social media bots, and valueless content. Watermarking
is a simple and effective strategy for mitigating such harms by enabling the
detection and documentation of LLM-generated text. Yet a crucial question
remains: How reliable is watermarking in realistic settings in the wild? There,
watermarked text may be modified to suit a user's needs, or entirely rewritten
to avoid detection. We study the robustness of watermarked text after it is
re-written by humans, paraphrased by a non-watermarked LLM, or mixed into a
longer hand-written document. We find that watermarks remain detectable even
after human and machine paraphrasing. While these attacks dilute the strength
of the watermark, paraphrases are statistically likely to leak n-grams or even
longer fragments of the original text, resulting in high-confidence detections
when enough tokens are observed. For example, after strong human paraphrasing
the watermark is detectable after observing 800 tokens on average, when setting
a 1e-5 false positive rate. We also consider a range of new detection schemes
that are sensitive to short spans of watermarked text embedded inside a large
document, and we compare the robustness of watermarking to other kinds of
detectors.
