Universally Optimal Watermarking Schemes for LLMs: from Theory to Practice

• URL: http://arxiv.org/abs/2410.02890v2
• Date: Thu, 10 Oct 2024 06:46:16 GMT
• Title: Universally Optimal Watermarking Schemes for LLMs: from Theory to Practice
• Authors: Haiyun He, Yepeng Liu, Ziqiao Wang, Yongyi Mao, Yuheng Bu,
• Abstract summary: Large Language Models (LLMs) boosts human efficiency but also poses misuse risks. We propose a novel theoretical framework for watermarking LLMs. We jointly optimize both the watermarking scheme and detector to maximize detection performance.
• Score: 35.319577498993354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) boosts human efficiency but also poses misuse risks, with watermarking serving as a reliable method to differentiate AI-generated content from human-created text. In this work, we propose a novel theoretical framework for watermarking LLMs. Particularly, we jointly optimize both the watermarking scheme and detector to maximize detection performance, while controlling the worst-case Type-I error and distortion in the watermarked text. Within our framework, we characterize the universally minimum Type-II error, showing a fundamental trade-off between detection performance and distortion. More importantly, we identify the optimal type of detectors and watermarking schemes. Building upon our theoretical analysis, we introduce a practical, model-agnostic and computationally efficient token-level watermarking algorithm that invokes a surrogate model and the Gumbel-max trick. Empirical results on Llama-13B and Mistral-8$\times$7B demonstrate the effectiveness of our method. Furthermore, we also explore how robustness can be integrated into our theoretical framework, which provides a foundation for designing future watermarking systems with improved resilience to adversarial attacks.

Related papers

• GaussMark: A Practical Approach for Structural Watermarking of Language Models [61.84270985214254]
  GaussMark is a simple, efficient, and relatively robust scheme for watermarking large language models. We show that GaussMark is reliable, efficient, and relatively robust to corruptions such as insertions, deletions, substitutions, and roundtrip translations.
  arXiv  Detail & Related papers  (2025-01-17T22:30:08Z)
• Robust Detection of Watermarks for Large Language Models Under Human Edits [27.678152860666163]
  We introduce a new method in the form of a truncated goodness-of-fit test for detecting watermarked text under human edits. We prove that the Tr-GoF test achieves optimality in robust detection of the Gumbel-GoF watermark. We also show that the Tr-GoF test attains the highest detection efficiency rate in a certain regime of moderate text modifications.
  arXiv  Detail & Related papers  (2024-11-21T06:06:04Z)
• WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents [63.563031923075066]
  WaterSeeker is a novel approach to efficiently detect and locate watermarked segments amid extensive natural text. It achieves a superior balance between detection accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-09-08T14:45:47Z)
• Learnable Linguistic Watermarks for Tracing Model Extraction Attacks on Large Language Models [20.44680783275184]
  Current watermarking techniques against model extraction attacks rely on signal insertion in model logits or post-processing of generated text. We propose a novel method for embedding learnable linguistic watermarks in Large Language Models (LLMs) Our approach subtly modifies the LLM's output distribution by introducing controlled noise into token frequency distributions, embedding a statistically identifiable watermark.
  arXiv  Detail & Related papers  (2024-04-28T14:45:53Z)
• A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules [27.678152860666163]
  We introduce a framework for reasoning about the statistical efficiency of watermarks and powerful detection rules. We derive optimal detection rules for watermarks under our framework.
  arXiv  Detail & Related papers  (2024-04-01T17:03:41Z)
• Towards Better Statistical Understanding of Watermarking LLMs [7.68488211412916]
  In this paper, we study the problem of watermarking large language models (LLMs) We consider the trade-off between model distortion and detection ability and it as a constrained optimization problem based on the green-red list of Kirchenbauer et al. We develop an online dual gradient ascent watermarking algorithm in light of this optimization formulation and prove its optimality between model distortion and detection ability.
  arXiv  Detail & Related papers  (2024-03-19T01:57:09Z)
• Duwak: Dual Watermarks in Large Language Models [49.00264962860555]
  We propose, Duwak, to enhance the efficiency and quality of watermarking by embedding dual secret patterns in both token probability distribution and sampling schemes. We evaluate Duwak extensively on Llama2, against four state-of-the-art watermarking techniques and combinations of them.
  arXiv  Detail & Related papers  (2024-03-12T16:25:38Z)
• An Unforgeable Publicly Verifiable Watermark for Large Language Models [84.2805275589553]
  Current watermark detection algorithms require the secret key used in the watermark generation process, making them susceptible to security breaches and counterfeiting during public detection. We propose an unforgeable publicly verifiable watermark algorithm named UPV that uses two different neural networks for watermark generation and detection, instead of using the same key at both stages.
  arXiv  Detail & Related papers  (2023-07-30T13:43:27Z)
• Provable Robust Watermarking for AI-Generated Text [41.5510809722375]
  We propose a robust and high-quality watermark method, Unigram-Watermark. We prove that our watermark method enjoys guaranteed generation quality, correctness in watermark detection, and is robust against text editing and paraphrasing.
  arXiv  Detail & Related papers  (2023-06-30T07:24:32Z)
• Exploring Structure Consistency for Deep Model Watermarking [122.38456787761497]
  The intellectual property (IP) of Deep neural networks (DNNs) can be easily stolen'' by surrogate model attack. We propose a new watermarking methodology, namely structure consistency'', based on which a new deep structure-aligned model watermarking algorithm is designed.
  arXiv  Detail & Related papers  (2021-08-05T04:27:15Z)
• Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models [72.9364216776529]
  We propose a novel watermark removal attack from a different perspective. We design a simple yet powerful transformation algorithm by combining imperceptible pattern embedding and spatial-level transformations. Our attack can bypass state-of-the-art watermarking solutions with very high success rates.
  arXiv  Detail & Related papers  (2020-09-18T09:14:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.