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A text watermark may remain detectable after some edits or paraphrases, but translation and targeted rewrites can sharply weaken particular watermarking methods. To check a specific text, compare the original and each transformed version with the detector designed for that watermark, using consistent settings and recording text length. A negative result means that detector did not find its signal under those conditions; it 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 watermarking method and sought by a corresponding detector. A generic AI-text detector is not automatically able to detect that signal. The useful question is therefore not simply “is there a watermark?” but “does this detector find evidence of this watermark in this version of the text, at this length and threshold?”
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Published results vary by method and transformation. In a 2024 ACL study, He and coauthors found inconsistent cross-lingual behavior among the methods they tested. Their Cross-lingual Watermark Removal Attack (CWRA) reduced AUC to a random-guessing level without reported performance loss in their experiments. That result does not establish that every translation defeats every watermark. Read the ACL paper.
Other studies show that paraphrasing need not erase a signal. Kirchenbauer and coauthors 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 remained. Under their specified conditions, detection after strong human paraphrasing took an average of 800 observed tokens at a false-positive rate of 1e-5. This is a study result, not a universal minimum length. Read the ICLR paper.
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How to check a text step by step
- Identify the watermark and detector. Find out which watermarking method is in question and use the detector intended to recognize it. A result from an unrelated AI-writing detector does not answer whether a particular watermark survived.
- Save the unmodified text and its baseline result. Run the relevant detector on the original. Record its exact score or decision, the detector settings, and—if available—the decision threshold or false-positive rate.
- Make one transformation at a time. Create separate versions for ordinary editing, paraphrasing, translation into a named language, and translation back if that is relevant. Keep intermediate versions rather than testing only the final output; otherwise, you cannot tell which step changed the evidence.
- Test every version consistently. Use the same detector and settings on each copy. Record how many tokens each version contains, since a shorter text provides less material for detection.
- Describe the finding narrowly. Report whether the detector’s evidence changed under the stated conditions. A negative detection is not proof that the text was never watermarked, and watermark detection alone is not a finding about authorship.
What published attack results show
Translation can defeat some tested signals
He and coauthors’ 2024 cross-lingual study found that the watermarking methods they evaluated did not behave consistently across languages. Their CWRA experiment brought AUC down to random-guessing level without reported performance loss. AUC describes how well a detector separates watermarked from unwatermarked text across thresholds; a random-guessing-level AUC indicates that separation was lost in that experiment. It is not a survival rate for all systems or language pairs.
Paraphrases can retain detectable fragments
Kirchenbauer and coauthors’ 2024 reliability study found detectable watermarks after human and machine paraphrasing in the settings they examined. Their explanation includes paraphrases retaining n-grams or longer fragments from the source. Their 800-token average after strong human paraphrasing applies to their study’s detector and a 1e-5 false-positive rate, not to every watermark or text.
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Targeted rewriting can break particular methods
Cheng and coauthors’ 2025 ICML paper reports that its Self-Information Rewrite Attack (SIRA) achieved nearly 100% attack success across seven recent watermarking methods studied, at a reported experimental cost of $0.88 per million tokens. Those figures describe that paper’s attack and tested systems; the cost is not a general commercial price, and the result does not establish that all watermarking methods fail under rewriting. Read the ICML paper.
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Zhao and coauthors’ 2024 ICLR paper presents Unigram-Watermark and theoretical robustness claims for editing and paraphrasing, alongside experiments on three LLMs and two datasets. Those results concern that method and those experiments; they do not show that unrelated watermark schemes share the same robustness. Read the ICLR paper.
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What to record when comparing versions
A useful comparison needs enough context to make its result interpretable. Include the transformation and language direction, the watermark and detector, detector threshold or false-positive rate, token count, score or other detection measure, and whether the meaning was preserved.
- Transformation: light editing, paraphrasing, translation, or round-trip translation.
- Language: source language and target language; identify each direction for translation and back-translation.
- Method: watermark scheme and its detector, rather than a general AI-text classifier.
- Decision conditions: detector settings, threshold, and false-positive rate if reported.
- Text and outcome: token count, score or AUC where available, and whether the transformed text preserved the original meaning.
These details matter because the cited studies test specific methods, models, datasets, lengths, and transformations. There is no universal threshold or survival percentage established by these results. The cited findings come from research papers, not hands-on tests of a reader’s text or a current commercial detector.
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