用分块令牌引导生成，精准去除水印同时保持图像结构一致。
TokenPure: Watermark Removal through Tokenized Appearance and Structural Guidance
- 将图像拆为视觉与结构两类令牌，联合指导去水印生成。
- 在多个数据集上，水印移除效果和图像保真度均领先现有方法。
- 适合需要高保真内容恢复的数字版权保护场景。
在数字经济时代，数字水印是大量可复制内容（包括AI生成内容及其他虚拟资产）所有权证明的关键依据。设计能抵御各类攻击和处理操作的鲁棒水印尤为重要。本文提出TokenPure，一种基于扩散Transformer的新框架，用于高效且一致地移除水印。该方法通过基于令牌的条件重建，解决了彻底破坏水印与保持内容一致性之间的权衡问题。其将含水印图像分解为两组互补的令牌：用于纹理的视觉令牌和用于几何结构的结构令牌。这两类令牌共同作为扩散过程的条件，使模型能够合成无水印、细节精细且结构完整的图像。全面实验表明，TokenPure在水印移除与重建保真度方面达到当前最优水平，在感知质量与内容一致性上显著优于现有基线方法。
原文摘要 · Abstract (English)
In the digital economy era, digital watermarking serves as a critical basis for ownership proof of massive replicable content, including AI-generated and other virtual assets. Designing robust watermarks capable of withstanding various attacks and processing operations is even more paramount. We introduce TokenPure, a novel Diffusion Transformer-based framework designed for effective and consistent watermark removal. TokenPure solves the trade-off between thorough watermark destruction and content consistency by leveraging token-based conditional reconstruction. It reframes the task as conditional generation, entirely bypassing the initial watermark-carrying noise. We achieve this by decomposing the watermarked image into two complementary token sets: visual tokens for texture and structural tokens for geometry. These tokens jointly condition the diffusion process, enabling the framework to synthesize watermark-free images with fine-grained consistency and structural integrity. Comprehensive experiments show that TokenPure achieves state-of-the-art watermark removal and reconstruction fidelity, substantially outperforming existing baselines in both perceptual quality and consistency.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。