Robust image watermarking that can resist camera shooting has become an active research topic in recent years due to the increasing demand for preventing sensitive information displayed on computer screens from being captured. However, many mainstream schemes require human assistance during the watermark detection process and cannot adapt to scenarios that require processing a large number of images. Although deep learning-based schemes enable end-to-end watermark embedding and detection, their limited generalization ability makes them vulnerable to failure in complex scenarios. In this paper, we propose a carefully crafted watermarking system that can resist camera shooting. The proposed scheme deals with two important problems: automatic watermark localization (AWL) and automatic watermark detection (AWD). AWL automatically identifies the region of interest (RoI), which contains watermark information, in the camera-shooting image by analyzing the local statistical characteristics. Meanwhile, AWD extracts the hidden watermark from the identified RoI after applying perspective correction. Compared with previous works, the proposed scheme is fully automatic, making it ideal for application scenarios. Furthermore, the proposed scheme is not limited to any specific watermark embedding strategy, allowing for improvements in the watermark embedding and extraction procedure. Extensive experimental results and analysis show that the embedded watermark can be automatically and reliably extracted from the camera-shooting image in different scenarios, demonstrating the superiority and applicability of the proposed approach.

翻译：近年来，随着防止计算机屏幕显示敏感信息被拍摄的需求日益增长，能够抵抗拍摄攻击的鲁棒图像水印已成为活跃的研究方向。然而，现有主流方案在水印检测过程中通常需要人工辅助，难以适应大规模图像处理场景。尽管基于深度学习的方法实现了端到端的水印嵌入与检测，但其泛化能力有限，在复杂场景下易失效。本文提出一种精心设计的抗拍摄水印系统，着重解决两个关键问题：自动水印定位（AWL）与自动水印检测（AWD）。AWL通过分析局部统计特征，自动识别拍摄图像中包含水印信息的感兴趣区域（RoI）；AWD则在透视校正后从识别到的RoI中提取隐藏水印。相较于现有方案，本方法具备全自动特性，可理想适配实际应用场景。同时，本方法不限于特定水印嵌入策略，能够兼容水印嵌入与提取流程的改进。大量实验结果与分析表明，本方法可在不同场景下从拍摄图像中自动可靠地提取嵌入水印，验证了所提方案在鲁棒性与适用性方面的优势。