Watermarking is a crucial tool for safeguarding copyrights and can serve as a more aesthetically pleasing alternative to QR codes. In recent years, watermarking methods based on deep learning have proved superior robustness against complex physical distortions than traditional watermarking methods. However, they have certain limitations that render them less effective in practice. For instance, current solutions necessitate physical photographs to be rectangular for accurate localization, cannot handle physical bending or folding, and require the hidden area to be completely captured at a close distance and small angle. To overcome these challenges, we propose a novel deep watermarking framework dubbed \textit{Aparecium}. Specifically, we preprocess secrets (i.e., watermarks) into a pattern and then embed it into the cover image, which is symmetrical to the final decoding-then-extracting process. To capture the watermarked region from complex physical scenarios, a locator is also introduced. Besides, we adopt a three-stage training strategy for training convergence. Extensive experiments demonstrate that \textit{Aparecium} is not only robust against different digital distortions, but also can resist various physical distortions, such as screen-shooting and printing-shooting, even in severe cases including different shapes, curvature, folding, incompleteness, long distances, and big angles while maintaining high visual quality. Furthermore, some ablation studies are also conducted to verify our design.

翻译：水印是保护版权的关键工具，并可作为比二维码更具美学价值的替代方案。近年来，基于深度学习的水印方法在对抗复杂物理失真方面展现出优于传统水印技术的鲁棒性。然而，现有方案存在某些局限性使其在实践中效果不佳：例如，当前解决方案要求实体照片必须为矩形以实现精确定位，无法处理物理弯曲或折叠，且需要隐藏区域在近距离和小角度下被完全捕捉。为攻克这些难题，我们提出名为《Aparecium》的新型深度水印框架。具体而言，我们将秘密信息（即水印）预处理为特定图案并嵌入载体图像，其过程与最终的解码-提取流程对称。为从复杂物理场景中捕获水印区域，我们引入定位模块。此外，采用三阶段训练策略以促进模型收敛。大量实验表明，《Aparecium》不仅对各类数字失真具有鲁棒性，更能抵御包括屏幕拍摄和打印拍摄在内的多种物理失真——即使面临不同形状、曲率、折叠、不完整性、远距离及大角度等极端情况，仍能保持高视觉质量。同时，消融研究进一步验证了我们的设计有效性。