ScreenMark: Watermarking Arbitrary Visual Content on Screen
Xiujian Liang, Gaozhi Liu, Yichao Si, Xiaoxiao Hu, Zhenxing Qian
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.
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它引用的顶会 Paper11
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 被引用 13,211 次
- MBRS: Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG CompressionZhaoyang Jia, Han Fang, Weiming ZhangACM MM 2021 · 被引用 251 次
- Robust Invertible Image SteganographyYoumin Xu, Chong Mou, Yujie Hu, Jingfen Xie 等CVPR 2022 · 被引用 151 次
- PIMoG: An Effective Screen-shooting Noise-Layer Simulation for Deep-Learning-Based Watermarking NetworkHan Fang, Zhaoyang Jia, Zehua Ma, Ee-Chien Chang 等ACM MM 2022 · 被引用 127 次
- Flow-Based Robust Watermarking with Invertible Noise Layer for Black-Box DistortionsHan Fang, Yupeng Qiu, Kejiang Chen, Jiyi Zhang 等AAAI 2023 · 被引用 73 次
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