FlowMark用自动预测的掩码实现强鲁棒视频水印，抗压缩和编辑。
FlowMark: Mask-Guided Video Watermarking
- 通过掩码预测网络自动找最佳嵌入区域，无需人工标注。
- 可嵌入128比特信息，峰值信噪比达50.08 dB，抗压缩与编辑能力强。
- 适合内容溯源、时间真实性验证等版权保护场景。
我们提出FlowMark，一种由自动预测对象掩码引导的视频水印框架。与以往需用户提供掩码的区域方法不同，FlowMark通过专用掩码预测网络学习最优嵌入区域。其端到端可训练架构结合区域感知编码与噪声增强训练，有效抵御压缩、几何变换及内容变化，同时保持高感知质量。自适应掩码使水印信号与自然视频动态一致，显著减少视觉闪烁。除压缩鲁棒性外，FlowMark在视频原生时序编辑（如帧交换、插入、删除、重采样与插值）及真实社交平台分发流程（如YouTube、Facebook重新编码）下仍能可靠恢复水印。在图像与视频数据集上的实验表明，该方法可稳定嵌入128比特消息，峰值信噪比最高达50.08 dB，适用于内容溯源、时间真实性验证与视频完整性保护。
原文摘要 · Abstract (English)
We present FlowMark, a video watermarking framework guided by automatically predicted object masks. In contrast to prior region-based approaches that require user-supplied mask guidance, FlowMark learns to identify optimal regions for watermark embedding through a dedicated Mask Predictor network. Our end-to-end trainable architecture combines region-aware encoding with noise-augmented training to ensure robustness against compression, geometric transformations, and content variation, while preserving high perceptual quality. Our content-adaptive masking keeps watermark signals coherent with natural video dynamics, effectively eliminating perceptual flicker. Beyond compression robustness, FlowMark maintains reliable watermark recovery under video-native temporal edits (e.g., frame swap, insertion, deletion, resampling, and interpolation) and real-world social media distribution pipelines (e.g., YouTube and Facebook re-encoding). Experimental results on both image and video datasets show that FlowMark reliably embeds $128$-bit messages with up to $50.08$ dB PSNR, offering strong performance for content provenance, temporal authenticity verification, and video integrity protection.
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