title: Robust Multi-bit Text Watermark with LLM-based Paraphrasers
summary: We propose an imperceptible multi-bit text watermark embedded by paraphrasing
  with LLMs. We fine-tune a pair of LLM paraphrasers that are designed to behave
  differently so that their paraphrasing difference reflected in the text
  semantics can be identified by a trained decoder. To embed our multi-bit
  watermark, we use two paraphrasers alternatively to encode the pre-defined
  binary code at the sentence level. Then we use a text classifier as the decoder
  to decode each bit of the watermark. Through extensive experiments, we show
  that our watermarks can achieve over 99.99\% detection AUC with small (1.1B)
  text paraphrasers while keeping the semantic information of the original
  sentence. More importantly, our pipeline is robust under word substitution and
  sentence paraphrasing perturbations and generalizes well to
  out-of-distributional data. We also show the stealthiness of our watermark with
  LLM-based evaluation. We open-source the code:
  https://github.com/xiaojunxu/multi-bit-text-watermark.
paper_authors._id: 6756459a8e874bcf29364ca1
paper_authors.name: Xiaojun Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-09T11:06:12.238Z
paper_authors._id: 675644c7d83c390221b45a61
paper_authors.avatarUrl: /avatars/ee2d659662e39a9adf3ea859fbb8d051.svg
paper_authors.fullname: Xiaojun Xu
paper_authors.type: user
paper_authors.user: xiaojunxu
paper_authors._id: 6756459a8e874bcf29364ca2
paper_authors.name: Jinghan Jia
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-10T13:10:20.552Z
paper_authors._id: 62b35eb9003cd12329d748a4
paper_authors.avatarUrl: /avatars/871cedeb9aa62c5ac57d54287b12afa7.svg
paper_authors.fullname: Jinghan Jia
paper_authors.type: user
paper_authors.user: flyingbugs
paper_authors._id: 6756459a8e874bcf29364ca3
paper_authors.name: Yuanshun Yao
paper_authors._id: 6756459a8e874bcf29364ca4
paper_authors.name: Yang Liu
paper_authors._id: 6756459a8e874bcf29364ca5
paper_authors.name: Hang Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-10T13:10:59.902Z
paper_authors._id: 61342a4b488458a484dee6c4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1630808595161-noauth.png
paper_authors.fullname: Hang Li
paper_authors.type: user
paper_authors.user: hanglics
paper_title: Robust Multi-bit Text Watermark with LLM-based Paraphrasers
paper_summary: We propose an imperceptible multi-bit text watermark embedded by paraphrasing
  with LLMs. We fine-tune a pair of LLM paraphrasers that are designed to behave
  differently so that their paraphrasing difference reflected in the text
  semantics can be identified by a trained decoder. To embed our multi-bit
  watermark, we use two paraphrasers alternatively to encode the pre-defined
  binary code at the sentence level. Then we use a text classifier as the decoder
  to decode each bit of the watermark. Through extensive experiments, we show
  that our watermarks can achieve over 99.99\% detection AUC with small (1.1B)
  text paraphrasers while keeping the semantic information of the original
  sentence. More importantly, our pipeline is robust under word substitution and
  sentence paraphrasing perturbations and generalizes well to
  out-of-distributional data. We also show the stealthiness of our watermark with
  LLM-based evaluation. We open-source the code:
  https://github.com/xiaojunxu/multi-bit-text-watermark.
paper_ai_summary: Proposed method uses fine-tuned LLMs for sentence-level text watermarking, ensuring imperceptibility and robust detection.
paper_ai_keywords: LLMs paraphrasers text watermark binary code text classifier detection AUC word substitution sentence paraphrasing out-of-distributional data LLM-based evaluation