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SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation

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arxiv 2310.03991 v2 pith:GRWTILAK submitted 2023-10-06 cs.CL

Abe Bohan Hou , Jingyu Zhang , Tianxing He , Yichen Wang , Yung-Sung Chuang , Hongwei Wang , Lingfeng Shen , Benjamin Van Durme
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Daniel Khashabi Yulia Tsvetkov
This is my paper · ORCID
classification cs.CL
keywords semanticalgorithmparaphrasebigramsentencewatermarkingattackattacks
verification ladder T0 review T1 audit T2 compute T3 formal
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Existing watermarking algorithms are vulnerable to paraphrase attacks because of their token-level design. To address this issue, we propose SemStamp, a robust sentence-level semantic watermarking algorithm based on locality-sensitive hashing (LSH), which partitions the semantic space of sentences. The algorithm encodes and LSH-hashes a candidate sentence generated by an LLM, and conducts sentence-level rejection sampling until the sampled sentence falls in watermarked partitions in the semantic embedding space. A margin-based constraint is used to enhance its robustness. To show the advantages of our algorithm, we propose a "bigram" paraphrase attack using the paraphrase that has the fewest bigram overlaps with the original sentence. This attack is shown to be effective against the existing token-level watermarking method. Experimental results show that our novel semantic watermark algorithm is not only more robust than the previous state-of-the-art method on both common and bigram paraphrase attacks, but also is better at preserving the quality of generation.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm

    cs.CR 2025-09 conditional novelty 7.0 of 10

    A trigger-tag watermark embedded by fine-tuning lets modified LLMs mark their own phishing outputs for cheap detection.

  2. GaussMark: A Practical Approach for Structural Watermarking of Language Models

    cs.CR 2025-01 conditional novelty 7.0 of 10

    GaussMark embeds a detectable watermark by adding per-generation Gaussian noise to one weight matrix and detecting gradient alignment with that noise.

  3. SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness

    cs.CR 2026-05 unverdicted novelty 6.5 of 10

    SAMark uses self-anchored semantic green regions, multi-channel hyperbolic scoring, and diversity-aware filtering to reach 90.2% TP@FP1% detection under paragraph paraphrasing while preserving text quality.

  4. Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey

    cs.CL 2025-02 conditional novelty 4.0 of 10

    A survey organizes current research on trustworthy RAG into six pillars, reliability, privacy, safety, fairness, explainability, and accountability, and maps methods, metrics, and open problems for each.

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