Evaluating the Robustness and Accuracy of Text Watermarking Under Real-World Cross-Lingual Manipulations
On the map: LLM safety and security → Watermarking generated content and models
Abstract (arXiv)
We present a study to benchmark representative watermarking methods in cross-lingual settings. The current literature mainly focuses on the evaluation of watermarking methods for the English language. However, the literature for evaluating watermarking in cross-lingual settings is scarce. This results in overlooking important adversary scenarios in which a cross-lingual adversary could be in, leading to a gray area of practicality over cross-lingual watermarking. In this paper, we evaluate four watermarking methods in four different and vocabulary rich languages. Our experiments investigate the quality of text under different watermarking procedure and the detectability of watermarks with practical translation attack scenarios. Specifically, we investigate practical scenarios that an adversary with cross-lingual knowledge could take, and evaluate whether current watermarking methods are suitable for such scenarios. Finally, from our findings, we draw key insights about watermarking in cross-lingual settings.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security. Builds on 19 of its 35 references that are in the map. First posted on arXiv 23 Feb 2025.
Builds on
19 of 35 references.
Closest ancestors · 7
Same direction, newest first
- 2024An Entropy-based Text Watermarking Detection Method20 Mar
- 2024No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices25 Feb
- 2024k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text17 Feb
- 2023Robust Distortion-free Watermarks for Language Models28 Jul
- 2023Provable Robust Watermarking for AI-Generated Text30 Jun
- 2023Who Wrote this Code? Watermarking for Code Generation24 May
- 2023Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense23 Mar
Other references · 12
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 705
- 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 490
- 2018Texygen: A Benchmarking Platform for Text Generation Modelscited by 484
- 2023Large Language Models Are Not Robust Multiple Choice Selectorscited by 454
- 2024Justice or Prejudice? Quantifying Biases in LLM-as-a-Judgecited by 354
- 2020Weight Poisoning Attacks on Pre-trained Modelscited by 318
- 2021Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Triggercited by 203
- 2020Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hidingcited by 121
- 2023AceGPT, Localizing Large Language Models in Arabiccited by 75
- 2022ESTAS: Effective and Stable Trojan Attacks in Self-supervised Encoders with One Target Unlabelled Samplecited by 9
- 2023TrojFSP: Trojan Insertion in Few-shot Prompt Tuningcited by 9
Built on by
Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2026Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarkingcited by 2
- 2026SIF: Semantically In-Distribution Fingerprints for Large Vision-Language Modelscited by 2
- 2025Is Multilingual LLM Watermarking Truly Multilingual? Scaling Robustness to 100+ Languages via Back-Translationcited by 1
- 2026Linguistics-Aware Non-Distortionary LLM Watermarkingcited by 0
- 2026Linguistics-Aware Non-Distortionary LLM Watermarking30 May 2026
- 2026SIF: Semantically In-Distribution Fingerprints for Large Vision-Language Models18 Apr 2026
- 2026Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking15 Apr 2026
- 2025Is Multilingual LLM Watermarking Truly Multilingual? Scaling Robustness to 100+ Languages via Back-Translation20 Oct 2025