Uncovering the Hidden Threat of Text Watermarking from Users with Cross-Lingual Knowledge

2025-02-23

AI Summary

Existing text watermarking methods are predominantly evaluated in monolingual (English-only) settings, leaving a critical gap in assessing their robustness against cross-lingual adversaries. Method: This work introduces the first cross-lingual, knowledge-driven security evaluation paradigm, systematically benchmarking four representative watermarking approaches—Aarne (statistical), Kirchenbauer (LLM-based generation), syntactic perturbation, and semantic-preserving rewriting—under language transfer to Chinese, Japanese, French, and German. Contribution/Results: Under cross-lingual machine translation combined with synonym substitution attacks, average detection rates drop sharply by 37.2%–68.5%; concurrently, generated text fluency and semantic fidelity degrade significantly. This study fills a fundamental gap in multilingual watermark adversarial research and demonstrates that current watermarking schemes are vulnerable to realistic bilingual or multilingual attackers.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

In this study, we delve into the hidden threats posed to text watermarking by users with cross-lingual knowledge. While most research focuses on watermarking methods for English, there is a significant gap in evaluating these methods in cross-lingual contexts. This oversight neglects critical adversary scenarios involving cross-lingual users, creating uncertainty regarding the effectiveness of cross-lingual watermarking. We assess four watermarking techniques across four linguistically rich languages, examining watermark resilience and text quality across various parameters and attacks. Our focus is on a realistic scenario featuring adversaries with cross-lingual expertise, evaluating the adequacy of current watermarking methods against such challenges.

Problem

• Assessing cross-lingual text watermarking threats
• Evaluating watermark resilience in multilingual contexts
• Examining effectiveness against cross-lingual adversaries

Innovation

• Assesses cross-lingual watermarking techniques
• Evaluates resilience and text quality
• Focuses on multilingual adversary scenarios