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LuYu Yuan

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2 papers

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AAAI Conference 2026 Conference Paper

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

Nan Sun
Yuxing Lu
Han Fang
Hefei Ling
Sijing Xie
LuYu Yuan
Chengxin Zhao

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.

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NeurIPS Conference 2025 Conference Paper

Ultra-high Resolution Watermarking Framework Resistant to Extreme Cropping and Scaling

Nan Sun
LuYu Yuan
Han Fang
Yuxing Lu
Hefei Ling
Sijing Xie
Chengxin Zhao

Recent developments in DNN-based image watermarking techniques have achieved impressive results in protecting digital content. However, most existing methods are constrained to low-resolution images as they need to encode the entire image, leading to prohibitive memory and computational costs when applied to high-resolution images. Moreover, they lack robustness to distortions prevalent in large-image transmission, such as extreme scaling and random cropping. To address these issues, we propose a novel watermarking method based on implicit neural representations (INRs). Leveraging the properties of INRs, our method employs resolution-independent coordinate sampling mechanism to generate watermarks pixel-wise, achieving ultra-high resolution watermark generation with fixed and limited memory and computational resources. This design ensures strong robustness in watermark extraction, even under extreme cropping and scaling distortions. Additionally, we introduce a hierarchical multi-scale coordinate embedding and a low-rank watermark injection strategy to ensure high-quality watermark generation and robust decoding. Experimental results demonstrate that our method significantly outperforms existing schemes in terms of both robustness and computational efficiency while preserving high image quality. Our approach achieves an accuracy greater than 98\% in watermark extraction with only 0. 4\% of the image area in 2K images. These results highlight the effectiveness of our method, making it a promising solution for large-scale and high-resolution image watermarking applications.

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