一种高效且有效的视频生成模型知识产权保护水印方案

中文摘要 AI 辅助

视频生成模型（VGMs）的快速发展使得生成高度逼真的合成视频成为可能，同时也引发了对这些模型知识产权（IPR）的担忧。特别是，两个密切相关的取证任务在很大程度上仍未得到解决：合成视频验证（确定视频是否由受保护的VGM生成）和模型所有权验证（确定可疑VGM是否为受保护VGM的未经授权副本）。在本文中，我们提出了一种新的生成内水印方案，可以解决这两个验证任务。首先，提出了一种名为VidMark的新型视频水印网络，该网络结合了两尺度离散小波变换（DWT）分解和全局时间注意力模块（GTAB），以增强水印的鲁棒性和不可感知性。其次，我们提出了一种解码器引导的微调过程。通过利用冻结的VidMark解码器，该过程使VGM能够合成携带不可感知、鲁棒且模型特定水印的视频。最后，建立了两个验证框架来执行合成视频验证和模型所有权验证。在代表性VGM上的大量实验表明，所提出的方案在两个任务上均实现了超过99%的水印提取准确率和100%的验证准确率，且对视频生成质量的影响可忽略不计。此外，水印对一系列全面的视频级和模型级攻击表现出很强的鲁棒性。

英文摘要

The rapid development of video generative models (VGMs) has enabled the generation of highly realistic synthetic videos, raising concerns about the intellectual property rights (IPR) of these models. In particular, two closely related forensic tasks remain largely unaddressed: synthetic video verification (determining whether a video was generated by a protected VGM) and model ownership verification (determining whether a suspect VGM is an unauthorized copy of a protected VGM). In this paper, we propose a new in-generation watermarking scheme that can address the two verification tasks. First, a novel video watermarking network named VidMark is presented, which incorporates a two-scale discrete wavelet transform (DWT) decomposition and a global temporal attention block (GTAB) to enhance watermark robustness and imperceptibility. Second, we present a decoder-guided fine-tuning procedure. By leveraging the frozen VidMark decoder, this process enables VGMs to synthesize videos carrying an imperceptible, robust, and model-specific watermark. Finally, two verification frameworks are established to perform synthetic video verification and model ownership verification. Extensive experiments on representative VGMs demonstrate that the proposed scheme achieves over 99% watermark extraction accuracy and 100% verification accuracy on both tasks, with negligible impact on video generation quality. Furthermore, the watermarks exhibit strong robustness against a comprehensive range of video-level and model-level attacks.

发表机构

• Sun Yat-sen University（中山大学）
• Guangdong Provincial Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
• University of Florence（佛罗伦萨大学）
• University of Siena（锡耶纳大学）
• Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））

机构由 AI 辅助整理，请以论文原文为准。