ScreenMark：屏幕任意视觉内容水印
ScreenMark: Watermarking Arbitrary Visual Content on Screen
AI总结：
ScreenMark提出三阶段渐进式水印框架，结合扩散原理、预乘Alpha混合及渐进失真器，保护任意屏幕内容，实验证明其鲁棒性、隐蔽性和实用性。
摘要：
数字水印在保护多媒体内容方面已显示出其有效性。然而，现有的水印技术主要针对特定媒体类型定制，这使得它们在保护计算机屏幕上显示的内容方面效果不佳，因为屏幕内容通常是多模态且动态的。视觉屏幕内容（VSC）尤其容易通过截图遭受盗窃和泄露，而当前的水印方法未能充分解决这一漏洞。为应对这些挑战，我们提出了ScreenMark，一种专门为任意VSC保护设计的鲁棒且实用的水印方法。ScreenMark采用三阶段渐进式水印框架。首先，受扩散原理启发，我们初始化常规水印信息与不规则水印图案之间的相互转换。随后，利用预乘Alpha混合技术将这些图案与屏幕内容集成，并辅以预训练的屏幕解码器以实现准确的水印检索。渐进式复杂的失真器增强了水印在真实截图场景中的鲁棒性。最后，模型在联合级别失真器的指导下进行微调，以确保最佳性能。为验证ScreenMark的有效性，我们构建了一个包含来自不同设备和分辨率的100,000张截图的数据集。在不同数据集上进行的大量实验证实了该方法在鲁棒性、不可感知性和实际适用性方面的优越性。
英文摘要：
Digital watermarking has shown its effectiveness in protecting multimedia content. However, existing watermarking is predominantly tailored for specific media types, rendering them less effective for the protection of content displayed on computer screens, which is often multi-modal and dynamic. Visual Screen Content (VSC), is particularly susceptible to theft and leakage through screenshots, a vulnerability that current watermarking methods fail to adequately address.To address these challenges, we propose ScreenMark, a robust and practical watermarking method designed specifically for arbitrary VSC protection. ScreenMark utilizes a three-stage progressive watermarking framework. Initially, inspired by diffusion principles, we initialize the mutual transformation between regular watermark information and irregular watermark patterns. Subsequently, these patterns are integrated with screen content using a pre-multiplication alpha blending technique, supported by a pre-trained screen decoder for accurate watermark retrieval. The progressively complex distorter enhances the robustness of the watermark in real-world screenshot scenarios. Finally, the model undergoes fine-tuning guided by a joint-level distorter to ensure optimal performance. To validate the effectiveness of ScreenMark, we compiled a dataset comprising 100,000 screenshots from various devices and resolutions. Extensive experiments on different datasets confirm the superior robustness, imperceptibility, and practical applicability of the method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
