用扩散模型结构化编码字符，抗干扰强且跨语言通用。
TRACE: Structure-Aware Character Encoding for Robust and Generalizable Document Watermarking
- 基于字符结构设计扩散初始化，自动定位可编辑区域
- 经跨媒体传输后，峰值信噪比提升超5 dB，提取准确率高5%
- 适合多语言多字体文档安全场景，通用性强
我们提出TRACE，一种利用扩散模型进行局部字符编码的结构感知框架。与依赖边缘特征或预定义码本的现有方法不同，TRACE利用字符结构的内在稳定性与统一表示，天然具备抗噪声能力。该框架包含三个关键组件：(1) 自适应扩散初始化，通过运动概率估计器（MPE）、目标点估计（TPE）和掩码绘制模型（MDM）自动识别控制点、目标点和编辑区域；(2) 有引导的扩散编码，实现选定点的精确移动；(3) 带专用损失函数的掩码区域替换，最大限度减少扩散过程后的特征变化。全面实验表明， ame{}在性能上优于当前最优方法，跨媒体传输后PSNR提升超过5 dB，提取准确率提高5%。该方法在多种语言和字体下表现良好，适用于实际文档安全应用。
原文摘要 · Abstract (English)
We propose TRACE, a structure-aware framework leveraging diffusion models for localized character encoding to embed data. Unlike existing methods that rely on edge features or pre-defined codebooks, TRACE exploits character structures that provide inherent resistance to noise interference due to their stability and unified representation across diverse characters. Our framework comprises three key components: (1) adaptive diffusion initialization that automatically identifies handle points, target points, and editing regions through specialized algorithms including movement probability estimator (MPE), target point estimation (TPE) and mask drawing model (MDM), (2) guided diffusion encoding for precise movement of selected point, and (3) masked region replacement with a specialized loss function to minimize feature alterations after the diffusion process. Comprehensive experiments demonstrate \name{}'s superior performance over state-of-the-art methods, achieving more than 5 dB improvement in PSNR and 5\% higher extraction accuracy following cross-media transmission. \name{} achieves broad generalizability across multiple languages and fonts, making it particularly suitable for practical document security applications.
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