GlyphShield: Document Watermarking for the Physical World via Vector Typeface Synthesis

Authors

  • Nan Sun Huazhong University of Science and Technology
  • Yuxing Lu Peking University
  • Han Fang National University of Singapore
  • Hefei Ling Huazhong University of Science and Technology
  • Sijing Xie Huazhong University of Science and Technology
  • LuYu Yuan Huazhong University of Science and Technology
  • Chengxin Zhao Huazhong University of Science and Technology

DOI:

https://doi.org/10.1609/aaai.v40i2.37073

Abstract

Document protection has become a critical issue for preventing unauthorized copying, distribution, and tampering. Document encryption is a proven solution, but it is not resistant to attacks from the physical world such as screenshots, printing and photographing. A common document protection technique is font-based watermarking, which embeds imperceptible information by using sets of visually similar glyphs to encode traceable data. However, due to the non-differentiable rendering process of vector fonts, these methods often rely on time-consuming and laborious manual design. To address this challenge, we present GlyphShield, an innovative end-to-end vector font watermarking framework. We resolve the non-differentiability challenge by simulating differentiable rasterization through the computation of Signed Distance Field (SDF) for Bézier curves in vector fonts. Besides, to handle complex vector font structures, a novel dual-branch vector encoder is employed to ensure high-quality font synthesis. Extensive experiments demonstrate that our approach ensures more natural and smoother message embedding while ensuring robustness against noise attacks in diverse scenarios. Additionally, our framework demonstrates strong generalization across various font styles and languages.

Published

2026-03-14

How to Cite

Sun, N., Lu, Y., Fang, H., Ling, H., Xie, S., Yuan, L., & Zhao, C. (2026). GlyphShield: Document Watermarking for the Physical World via Vector Typeface Synthesis. Proceedings of the AAAI Conference on Artificial Intelligence, 40(2), 1033-1041. https://doi.org/10.1609/aaai.v40i2.37073

Issue

Section

AAAI Technical Track on Application Domains II