CoreMark: Toward Robust and Universal Text Watermarking Technique
Not on the map. Why?
Abstract (arXiv)
Text watermarking schemes have gained considerable attention in recent years, yet still face critical challenges in achieving simultaneous robustness, generalizability, and imperceptibility. This paper introduces a new embedding paradigm,termed CORE, which comprises several consecutively aligned black pixel segments. Its key innovation lies in its inherent noise resistance during transmission and broad applicability across languages and fonts. Based on the CORE, we present a text watermarking framework named CoreMark. Specifically, CoreMark first dynamically extracts COREs from characters. Then, the characters with stronger robustness are selected according to the lengths of COREs. By modifying the thickness of the CORE, the hidden data is embedded into the selected characters without causing significant visual distortions. Moreover, a general plug-and-play embedding strength modulator is proposed, which can adaptively enhance the robustness for small font sizes by adjusting the embedding strength according to the font size. Experimental evaluation indicates that CoreMark demonstrates outstanding generalizability across multiple languages and fonts. Compared to existing methods, CoreMark achieves significant improvements in resisting screenshot, print-scan, and print camera attacks, while maintaining satisfactory imperceptibility.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 43 references that are in the map. First posted on arXiv 29 Jun 2025.
Builds on
5 of 43 references.
References · 5
- 2022Black-box Dataset Ownership Verification via Backdoor Watermarkingcited by 65
- 2021Tracing Text Provenance via Context-Aware Lexical Substitutioncited by 45
- 2021Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Modelcited by 30
- 2024PointNCBW: Towards Dataset Ownership Verification for Point Clouds via Negative Clean-label Backdoor Watermarkcited by 20
- 2025Mask Image Watermarkingcited by 9
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2025FontGuard: A Robust Font Watermarking Approach Leveraging Deep Font Knowledge
- 2025CEFW: A Comprehensive Evaluation Framework for Watermark in Large Language Models
- 2024Watermarking Language Models with Error Correcting Codes
- 2025DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models
- 2025BiMark: Unbiased Multilayer Watermarking for Large Language Models
- 2024PostMark: A Robust Blackbox Watermark for Large Language Models
- 2023A Survey of Text Watermarking in the Era of Large Language Models
- 2026ByteMark: A Unified Byte-Level Watermark for Detection Across Language Models