Revisiting the Robustness of Watermarking to Paraphrasing Attacks
- URL: http://arxiv.org/abs/2411.05277v1
- Date: Fri, 08 Nov 2024 02:22:30 GMT
- Title: Revisiting the Robustness of Watermarking to Paraphrasing Attacks
- Authors: Saksham Rastogi, Danish Pruthi,
- Abstract summary: Many recent watermarking techniques modify the output probabilities of LMs to embed a signal in the generated output that can later be detected.
We show that with access to only a limited number of generations from a black-box watermarked model, we can drastically increase the effectiveness of paraphrasing attacks to evade watermark detection.
- Score: 10.68370011459729
- License:
- Abstract: Amidst rising concerns about the internet being proliferated with content generated from language models (LMs), watermarking is seen as a principled way to certify whether text was generated from a model. Many recent watermarking techniques slightly modify the output probabilities of LMs to embed a signal in the generated output that can later be detected. Since early proposals for text watermarking, questions about their robustness to paraphrasing have been prominently discussed. Lately, some techniques are deliberately designed and claimed to be robust to paraphrasing. However, such watermarking schemes do not adequately account for the ease with which they can be reverse-engineered. We show that with access to only a limited number of generations from a black-box watermarked model, we can drastically increase the effectiveness of paraphrasing attacks to evade watermark detection, thereby rendering the watermark ineffective.
Related papers
- 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) - Watermark Smoothing Attacks against Language Models [40.02225709485305]
We introduce smoothing attacks and show that existing watermarking methods are not robust against minor modifications of text.
Our attack reveals a fundamental limitation of a wide range of watermarking techniques.
arXiv Detail & Related papers (2024-07-19T11:04:54Z) - 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) - Adaptive Text Watermark for Large Language Models [8.100123266517299]
It is challenging to generate high-quality watermarked text while maintaining strong security, robustness, and the ability to detect watermarks without prior knowledge of the prompt or model.
This paper proposes an adaptive watermarking strategy to address this problem.
arXiv Detail & Related papers (2024-01-25T03:57:12Z) - On the Learnability of Watermarks for Language Models [80.97358663708592]
We ask whether language models can directly learn to generate watermarked text.
We propose watermark distillation, which trains a student model to behave like a teacher model.
We find that models can learn to generate watermarked text with high detectability.
arXiv Detail & Related papers (2023-12-07T17:41:44Z) - Improving the Generation Quality of Watermarked Large Language Models
via Word Importance Scoring [81.62249424226084]
Token-level watermarking inserts watermarks in the generated texts by altering the token probability distributions.
This watermarking algorithm alters the logits during generation, which can lead to a downgraded text quality.
We propose to improve the quality of texts generated by a watermarked language model by Watermarking with Importance Scoring (WIS)
arXiv Detail & Related papers (2023-11-16T08:36:00Z) - On the Reliability of Watermarks for Large Language Models [95.87476978352659]
We study the robustness of watermarked text after it is re-written by humans, paraphrased by a non-watermarked LLM, or mixed into a longer hand-written document.
We find that watermarks remain detectable even after human and machine paraphrasing.
We also consider a range of new detection schemes that are sensitive to short spans of watermarked text embedded inside a large document.
arXiv Detail & Related papers (2023-06-07T17:58:48Z) - A Watermark for Large Language Models [84.95327142027183]
We propose a watermarking framework for proprietary language models.
The watermark can be embedded with negligible impact on text quality.
It can be detected using an efficient open-source algorithm without access to the language model API or parameters.
arXiv Detail & Related papers (2023-01-24T18:52:59Z) - 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.