A text watermark may survive some edits or paraphrases, but translation and targeted rewriting can weaken or defeat particular watermarking methods. The result depends on the watermark, its detector, the transformation and the amount of text available. To check a specific case, compare the original and each transformed version with the same watermark-specific detector and settings; a negative result does not prove the text was never watermarked.
What a watermark check can—and cannot—tell you
A text watermark is a signal embedded by a particular generation method and sought by a compatible detector. A generic AI-text detector is not automatically able to detect that signal. A watermark check therefore answers a narrower question: how much evidence the selected detector finds for its target watermark in this text under the settings used.
Published results vary by method and transformation. In a 2024 study, He et al. found inconsistent cross-lingual behavior among the watermarking methods they tested. Their Cross-lingual Watermark Removal Attack (CWRA) reduced AUC to random-guessing level without reported performance loss in their experiments. This finding is specific to those methods and experiments; it does not establish that every translation defeats every watermark. He et al., ACL 2024.
Paraphrasing does not necessarily erase a watermark. Kirchenbauer et al. reported that watermarks remained detectable after human and machine paraphrasing in their study; retained n-grams or longer fragments could support detection when enough tokens were observed. After strong human paraphrasing, their study reported detection after an average of 800 tokens at a 1e-5 false-positive rate. That is a result under the paper’s conditions, not a universal minimum text length. Kirchenbauer et al., ICLR 2024.
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Targeted rewriting can also be effective against particular schemes. Cheng et al. reported that their Self-Information Rewrite Attack achieved nearly 100% attack success on seven studied watermarking methods, at a reported experimental cost of $0.88 per million tokens. These figures describe that paper’s experiment, not a general success rate or commercial price. Cheng et al., ICML 2025.
Robustness claims likewise belong to the scheme studied. Zhao et al. present Unigram-Watermark and make theoretical robustness claims for editing and paraphrasing, with experiments on three LLMs and two datasets. Those results do not establish that other watermark schemes have the same properties. Zhao et al., ICLR 2024.
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How to check a particular text
- Identify the watermark and detector. Find out which watermarking method is alleged to have produced the text and which detector is designed to recognize it. If you do not know the method, a result from an unrelated detector cannot settle whether that watermark survives.
- Save the original and establish a baseline. Run the compatible detector on the unchanged text. Record its score or decision, detector settings, and—if reported—the decision threshold or false-positive rate.
- Change one thing at a time. Create separate versions for ordinary editing, paraphrasing, translation into a named language, and translation back, as relevant. Save each intermediate version rather than testing only the final result.
- Retest with the same settings. Run the same watermark detector on every version. Record the token count, since the amount of text available can affect detection strength.
- Describe the outcome narrowly. Report how the detector’s evidence changed for those versions and conditions. Do not treat the result as proof of AI authorship, human authorship or the text’s complete history.
What to record when comparing results
A useful comparison makes clear what changed and how detection was measured. Record the following for each original or transformed version:
- Transformation: light editing, paraphrasing, translation or round-trip translation.
- Languages: the source and target language for translation.
- Watermark and detector: the specific method and compatible detector, not simply a generic AI detector.
- Detector settings: threshold and false-positive rate where available, plus the score or other reported measure.
- Text length: token count for each version.
- Meaning: whether the transformation preserved the text’s meaning.
These details matter because the studies report results for particular methods, datasets, models and transformations. Without them, a statement such as “the watermark survived translation” is too broad to interpret.
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How to interpret a negative result
If the detector no longer identifies the watermark, the supported conclusion is that it did not detect sufficient evidence in that version under the settings used. That does not establish that the original lacked a watermark: editing, translation, paraphrasing, detector thresholds and text length can all affect the observed result. Conversely, a positive result indicates evidence for the detector’s target signal, not proof by itself of who wrote the text or how it was produced.
The available findings are research results, not a universal threshold or survival percentage and not hands-on tests of a reader’s text in a current commercial detector. Treat numerical findings as specific to their cited studies and avoid generalizing them to an untested detector or watermark.
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