The advent of high-quality video generation models has amplified the need for robust watermarking schemes that can be used to reliably detect and track the provenance of generated videos. Existing video watermarking methods based on both post-hoc and in-generation approaches fail to simultaneously achieve imperceptibility, robustness, and computational efficiency. This work introduces a novel framework for in-generation video watermarking called SPDMark (pronounced `SpeedMark') based on selective parameter displacement of a video diffusion model. Watermarks are embedded into the generated videos by modifying a subset of parameters in the generative model. To make the problem tractable, the displacement is modeled as an additive composition of layer-wise basis shifts, where the final composition is indexed by the watermarking key. For parameter efficiency, this work specifically leverages low-rank adaptation (LoRA) to implement the basis shifts. During the training phase, the basis shifts and the watermark extractor are jointly learned by minimizing a combination of message recovery, perceptual similarity, and temporal consistency losses. To detect and localize temporal modifications in the watermarked videos, we use a cryptographic hashing function to derive frame-specific watermark messages from the given base watermarking key. During watermark extraction, maximum bipartite matching is applied to recover the correct frame order, even from temporally tampered videos. Evaluations on both text-to-video and image-to-video generation models demonstrate the ability of SPDMark to generate imperceptible watermarks that can be recovered with high accuracy and also establish its robustness against a variety of common video modifications.

翻译：高质量视频生成模型的出现，增强了对鲁棒水印方案的需求，以可靠检测和追踪生成视频的来源。现有的基于后处理与生成中嵌入的视频水印方法，均未能同时实现不可感知性、鲁棒性与计算效率。本研究提出一种名为SPDMark（发音为`SpeedMark`）的新型生成中视频水印框架，其基于视频扩散模型的选择性参数位移。通过修改生成模型中的部分参数，将水印嵌入生成的视频中。为使问题可解，位移被建模为层间基位移的加性组合，最终组合由水印密钥索引。为实现参数高效性，本研究特别利用低秩自适应（LoRA）实现基位移。在训练阶段，通过最小化消息恢复、感知相似性与时序一致性损失的组合，联合学习基位移与水印提取器。为检测并定位水印视频中的时序篡改，我们使用密码学哈希函数从给定的基础水印密钥推导出帧特定水印消息。在水印提取过程中，应用最大二分图匹配以恢复正确的帧顺序，即使面对时序篡改的视频。在文本到视频与图像到视频生成模型上的评估表明，SPDMark能够生成不可感知的水印，并以高精度恢复水印，同时验证了其对多种常见视频修改的鲁棒性。