Lost in Overlap: Exploring Watermark Collision in LLMs

• URL: http://arxiv.org/abs/2403.10020v2
• Date: Wed, 14 Aug 2024 13:15:00 GMT
• Title: Lost in Overlap: Exploring Watermark Collision in LLMs
• Authors: Yiyang Luo, Ke Lin, Chao Gu,
• Abstract summary: We introduce watermark collision as a novel and general philosophy for watermark attacks. We provide a comprehensive demonstration that watermark collision poses a threat to all logit-based watermark algorithms.
• Score: 6.398660996031915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of large language models (LLMs) in generating content raises concerns about text copyright. Watermarking methods, particularly logit-based approaches, embed imperceptible identifiers into text to address these challenges. However, the widespread usage of watermarking across diverse LLMs has led to an inevitable issue known as watermark collision during common tasks, such as paraphrasing or translation. In this paper, we introduce watermark collision as a novel and general philosophy for watermark attacks, aimed at enhancing attack performance on top of any other attacking methods. We also provide a comprehensive demonstration that watermark collision poses a threat to all logit-based watermark algorithms, impacting not only specific attack scenarios but also downstream applications.

Related papers

• ESpeW: Robust Copyright Protection for LLM-based EaaS via Embedding-Specific Watermark [50.08021440235581]
  Embeds as a Service (Eding) is emerging as a crucial role in AI applications. Eding is vulnerable to model extraction attacks, highlighting the urgent need for copyright protection. We propose a novel embedding-specific watermarking (ESpeW) mechanism to offer robust copyright protection for Eding.
  arXiv  Detail & Related papers  (2024-10-23T04:34:49Z)
• De-mark: Watermark Removal in Large Language Models [59.00698153097887]
  We present De-mark, an advanced framework designed to remove n-gram-based watermarks effectively. Our method utilizes a novel querying strategy, termed random selection probing, which aids in assessing the strength of the watermark.
  arXiv  Detail & Related papers  (2024-10-17T17:42:10Z)
• Can Watermarked LLMs be Identified by Users via Crafted Prompts? [55.460327393792156]
  This work is the first to investigate the imperceptibility of watermarked Large Language Models (LLMs) We design an identification algorithm called Water-Probe that detects watermarks through well-designed prompts. Experiments show that almost all mainstream watermarking algorithms are easily identified with our well-designed prompts.
  arXiv  Detail & Related papers  (2024-10-04T06:01:27Z)
• Less is More: Sparse Watermarking in LLMs with Enhanced Text Quality [27.592486717044455]
  We present a novel type of watermark, Sparse Watermark, which aims to mitigate this trade-off by applying watermarks to a small subset of generated tokens distributed across the text. Our experimental results demonstrate that the proposed watermarking scheme achieves high detectability while generating text that outperforms previous watermarking methods in quality across various tasks.
  arXiv  Detail & Related papers  (2024-07-17T18:52:12Z)
• On Evaluating The Performance of Watermarked Machine-Generated Texts Under Adversarial Attacks [20.972194348901958]
  We first comb the mainstream watermarking schemes and removal attacks on machine-generated texts. We evaluate eight watermarks (five pre-text, three post-text) and twelve attacks (two pre-text, ten post-text) across 87 scenarios. Results indicate that KGW and Exponential watermarks offer high text quality and watermark retention but remain vulnerable to most attacks.
  arXiv  Detail & Related papers  (2024-07-05T18:09:06Z)
• Large Language Model Watermark Stealing With Mixed Integer Programming [51.336009662771396]
  Large Language Model (LLM) watermark shows promise in addressing copyright, monitoring AI-generated text, and preventing its misuse. Recent research indicates that watermarking methods using numerous keys are susceptible to removal attacks. We propose a novel green list stealing attack against the state-of-the-art LLM watermark scheme.
  arXiv  Detail & Related papers  (2024-05-30T04:11:17Z)
• Watermark Stealing in Large Language Models [2.1165011830664673]
  We show that querying the API of the watermarked LLM to approximately reverse-engineer a watermark enables practical spoofing attacks. We are the first to propose an automated WS algorithm and use it in the first comprehensive study of spoofing and scrubbing in realistic settings.
  arXiv  Detail & Related papers  (2024-02-29T17:12:39Z)
• Certified Neural Network Watermarks with Randomized Smoothing [64.86178395240469]
  We propose a certifiable watermarking method for deep learning models. We show that our watermark is guaranteed to be unremovable unless the model parameters are changed by more than a certain l2 threshold. Our watermark is also empirically more robust compared to previous watermarking methods.
  arXiv  Detail & Related papers  (2022-07-16T16:06:59Z)
• 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.