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Short answer: A local language model can weaken or remove some text-watermark signals, but success depends on the watermark, detector, text length and rewrite strategy. A rewrite that evades detection has not automatically preserved the original meaning, facts or nuance. The claimed “300 rewrites” experiment cannot be independently assessed without its model, watermark, detector, prompts and scoring records.
What a text watermark is actually detecting
A text watermark is a statistical signal embedded during generation. The signal may be placed in token choices, word sequences or a representation of sentence meaning. A detector then tests whether the finished passage contains that pattern strongly enough to exceed a selected threshold.
Token-level watermarks
Token-level systems bias generation toward selected token sets or patterns. Rephrasing can replace those tokens, especially when a rewrite changes sentence structure. However, paraphrases may retain distinctive n-grams or longer fragments, allowing some signal to survive.
Semantic-level watermarks
Semantic systems try to encode a signal in meaning-related representations rather than in exact wording. SemStamp uses locality-sensitive hashing and rejection sampling. Its authors report that a bigram paraphrase attack was effective against existing token-level methods while causing only minor degradation to SemStamp, along with better generation-quality preservation than the comparison method. That is a comparative result, not a guarantee that every SemStamp rewrite remains faithful.
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Why published results point in different directions
Watermark robustness is not a single property. Different papers use different watermark implementations, attacks, detectors, thresholds and passage lengths.
Paraphrasing can leave a detectable signal
The ICLR 2024 study On the Reliability of Watermarks for Large Language Models states: “We find that watermarks remain detectable even after human and machine paraphrasing.” In its tested setting, strong human paraphrasing produced an average of about 800 observed tokens at a false-positive rate of 1e-5. That figure describes the study’s detector and conditions; it is not a universal minimum length for detecting any watermark.
Targeted rewriting can defeat tested methods
The 2025 ICML paper Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks describes a Self-Information Rewrite Attack (SIRA). It uses token self-information to identify likely watermark-pattern tokens and targets them for paraphrasing. The authors report nearly 100% attack success against seven tested watermarking methods. They also report that the attack needs no access to the watermark algorithm or watermarked model and transfers to attack models, including mobile-level models. The paper reports a cost of $0.88 per million tokens for its study setup; that is not a general price for local inference or other rewrite systems.
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Broader evaluations show the setup matters
Watermark under Fire introduces WaterPark, a unified evaluation platform covering 10 watermarking methods and 12 representative attacks. Its scope illustrates why a result for one watermark or one attack should not be generalized to all local models and detectors.
These findings are compatible: ordinary paraphrasing may leave enough structure for detection, while a targeted attack designed to alter watermark-bearing choices can perform much better against particular methods.
Removing a signal is not the same as preserving meaning
Every credible rewrite evaluation needs two independent outcomes:
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- Detection outcome: whether the detector still flags the rewritten text, including its threshold and false-positive rate.
- Content outcome: whether the rewrite retains the original claims, entities, numbers, qualifiers, tone and intended implications.
A passage can fail detection because it was substantially changed, factually corrupted or shortened. Conversely, a highly faithful paraphrase can remain detectable. The SIRA paper itself cautions that its approach does not guarantee semantic preservation.
What a defensible 300-rewrite experiment must report
The number 300 is meaningful only if readers know what was counted and how each result was scored. A reproducible report should include the following:
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| Dimension | What to disclose | Why it changes the result |
|---|---|---|
| Watermark | Implementation and whether it is token-level or semantic-level | Different designs respond differently to paraphrase and targeted edits. |
| Detector | Detector name, decision threshold and false-positive rate | “Removed” depends on the detector and cutoff used. |
| Local model | Exact model and version, quantization and relevant inference settings | Models differ in vocabulary, capability and rewriting behavior. |
| Prompt and passes | Full rewrite prompt, style instructions, number of passes and whether the model knew a watermark was present | Targeted rewriting and ordinary editing are different attacks. |
| Source set | Number of source passages, domains and observed token counts | Detection power depends partly on text length and content. |
| Before/after detection | Detection scores before rewriting and after every pass | A final yes/no result hides how much signal was diluted. |
| Meaning assessment | Semantic similarity, factual-consistency checks and human judgments | Detection loss alone cannot establish fidelity. |
| Failures | Changed facts, omissions, hallucinations and rewrites that remained detectable | Reporting only successful removals creates survivorship bias. |
No published source identified here documents the claimed experiment’s local model, watermark, detector, prompts, passage set, rewrite count definition or quality rubric. “Three hundred rewrites” could mean 300 source texts, 300 attempts, or 300 outputs from repeated passes; those are not equivalent.
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How to read possible outcomes
Detection falls and meaning scores stay high
This is evidence that the particular model and prompt reduced detectability for the particular watermark under test while retaining the measured content properties. It does not show that other watermark designs or longer passages will behave the same way.
Detection falls but facts change
The rewrite is a successful evasion only in the narrow detector sense. It should not be described as meaning-preserving. A factuality audit should identify which claims, numbers, names or conditions changed.
Detection remains high despite fluent prose
Fluency is not evidence of watermark removal. The paraphrase may have retained token patterns, semantic features or enough original text for the detector to keep its score above threshold.
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Results vary by passage length
Short passages can produce unstable detector decisions, while longer passages may provide more evidence for a watermark. Report token counts rather than averaging texts of very different lengths without qualification.
Does using a local model change the conclusion?
Local inference changes who controls the model, prompts and data; it does not create a universal watermark-removal capability. The relevant variables remain the model’s rewriting behavior, the watermark design, the detector and the evaluation rubric. SIRA’s authors report transfer to attack models including mobile-level systems, but that finding is tied to the seven methods and setup they studied.
Likewise, neither the ICLR reliability study nor WaterPark establishes that every local model preserves meaning while defeating every detector. A local model can produce a private, repeatable experiment, but its results still need the same disclosures and controls as a hosted-model test.
Verdict
A local LLM can wash out some watermark signals without obviously mangling a passage, but current evidence does not support a blanket claim that it can reliably do so while preserving meaning. The strongest studies show both sides: watermarks can survive ordinary human or machine paraphrasing, while targeted attacks can defeat several tested methods. The 300-rewrite claim is not independently substantiated until its watermark, detector, model, prompts, text lengths and separate semantic and factual scores are published.
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