Yes, some AI text watermarks can be weakened or evaded, but no single edit is guaranteed to remove every watermark. Results depend on the watermark method, the text, the rewriting attack, and the detector. A detector’s result is evidence about the method and sample it tested—not a definitive judgment about who wrote the text.
What an AI text watermark is—and what it is not
A statistical text watermark is a signal associated with generated text that a compatible detection procedure can look for. Many watermarking methods alter a language model’s token-selection behavior during generation. Other methods add a signal after text has been generated. These approaches differ from a visible “AI-generated” label and from a general-purpose AI-text classifier.
That distinction matters: a detector built for one watermarking method may not detect another method, or any watermark at all. The EMNLP 2024 paper PostMark describes a post-hoc approach and notes that common generation-time techniques often require access to a model’s logits. Findings about one algorithm should not be treated as findings about every AI writing product.
Can a watermark be removed or bypassed?
Published research describes rewriting attacks intended to weaken or evade some watermarks. A 2026 ICML paper on the Bias-Inversion Rewriting Attack (BIRA) reports evasion rates above 99% across diverse watermarking schemes in its experiments, with substantially better semantic fidelity than prior baselines. That is a result for the paper’s evaluated attack and conditions—not a success-rate guarantee for ordinary users, commercial tools, or every watermark.
“Bypass” also covers more than one goal. A 2026 EACL paper distinguishes scrubbing, which aims to make watermarked text evade detection, from spoofing, which aims to make unwatermarked text appear watermarked. The paper reviews attacks that infer or exploit a watermark mechanism. These are research categories, not proof that a particular service can be defeated in a particular way.
Why paraphrasing does not give a universal answer
Rewriting can reduce a detectable signal, but paraphrased text can also retain n-grams or longer fragments from the original. In an ICLR 2024 reliability study, watermarks remained detectable after human and machine paraphrasing in some evaluated cases. The researchers reported an average of 800 tokens after strong human paraphrasing at a false-positive rate of 1e-5. This describes that study’s setting; it is not a universal minimum text length, a guarantee that shorter text cannot be detected, or a current specification for commercial detectors.
Evaluation design also changes what “robust” means. The ACL’s EMNLP 2025 WaterPark study by Liang et al. integrated 10 watermarkers and 12 representative attacks into a structured evaluation. Its breadth illustrates why watermark robustness is not one property with one score: different methods and attacks can produce different outcomes.
Researchers are also proposing more robust approaches. The EMNLP 2024 PostMark paper reports greater paraphrase robustness than its baselines across eight algorithms, five base LLMs, and three datasets, while examining a trade-off between text quality and robustness. The ICML 2026 PASA paper proposes semantic-level watermarking and reports robustness under strong paraphrasing in its evaluations. These results support claims about the proposed methods and their experiments, not a conclusion that watermarks as a whole now survive every rewrite.
How to assess a watermark or detector claim
Before relying on a claim that a watermark survived—or was removed—check what was actually tested. These questions help separate a paper’s experimental result from a broad claim about AI text:
- Which method? Was the watermark inserted during generation or added afterward, and does the detector support that specific scheme?
- Which attack and access? Was the text edited by a person, paraphrased by a model, or subjected to an attack with detector access, black-box queries, or information about the watermark?
- How much text and rewriting? Check the sample length and the extent of changes; a short passage and a substantially rewritten document are not interchangeable test cases.
- What detection threshold? Look for the detector’s threshold and false-positive setting. A result at one setting does not automatically transfer to another.
- What happened to meaning and quality? Evasion alone does not show that the rewritten passage preserved its meaning or met a useful quality standard.
WaterPark’s evaluation of multiple watermarkers and attacks, and the ICLR reliability study’s explicit token-count and false-positive conditions, show why these details matter when comparing results.
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What readers should infer from a detector result
- A positive result means the tested text matched a signal the detector was designed to recognize under its chosen settings. It is not, by itself, proof of authorship.
- A negative result does not establish that a passage was written without AI assistance: the detector may not support the watermark method, or the text may not retain a detectable signal.
- Ordinary editing may leave detectable traces in some cases, while research attacks can weaken some schemes. Neither “any edit removes it” nor “every watermark survives every rewrite” is supported as a universal rule.
The cited studies evaluate watermark detection and robustness; they do not establish a universal standard for deciding who authored a passage. Treat detector output as one limited piece of evidence, not an authorship verdict.
What writers should do when provenance matters
Do not count on minor edits to reliably remove a watermark, and do not assume that a detector will identify every AI-assisted passage. If a workplace, classroom, or publication has rules about AI use or disclosure, follow those rules. Keeping a clear record of drafting and revision can help explain how a piece was produced; that is practical guidance, not a result established by the watermark studies.
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The available conference research establishes that attacks and defenses exist and that results vary by scheme. It does not provide a verified, current inventory of watermark methods used by commercial AI writing services or the detector access those providers offer. Avoid attributing a specific watermark to a vendor without current, vendor-specific evidence.
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