LVMark: Robust Watermark for latent video diffusion models

Rapid advancements in generative models have made it possible to create hyper-realistic videos. As their applicability increases, their unauthorized use has raised significant concerns, leading to the growing demand for techniques to protect the ownership of the generative model itself. While existing watermarking methods effectively embed watermarks into image-generative models, they fail to account for temporal information, resulting in poor performance when applied to video-generative models. To address this issue, we introduce a novel watermarking method called LVMark, which embeds watermarks into video diffusion models. A key component of LVMark is a selective weight modulation strategy that efficiently embeds watermark messages into the video diffusion model while preserving the quality of the generated videos. To accurately decode messages in the presence of malicious attacks, we design a watermark decoder that leverages spatio-temporal information in the 3D wavelet domain through a cross-attention module. To the best of our knowledge, our approach is the first to highlight the potential of video-generative model watermarking as a valuable tool for enhancing the effectiveness of ownership protection in video-generative models.

WateRF: Robust Watermarks in Radiance Fields for Protection of Copyrights

The advances in the Neural Radiance Fields (NeRF) research offer extensive applications in diverse domains, but protecting their copyrights has not yet been researched in depth. Recently, NeRF watermarking has been considered one of the pivotal solutions for safely deploying NeRF-based 3D representations. However, existing methods are designed to apply only to implicit or explicit NeRF representations. In this work, we introduce an innovative watermarking method that can be employed in both representations of NeRF. This is achieved by fine-tuning NeRF to embed binary messages in the rendering process. In detail, we propose utilizing the discrete wavelet transform in the NeRF space for watermarking. Furthermore, we adopt a deferred back-propagation technique and introduce a combination with the patch-wise loss to improve rendering quality and bit accuracy with minimum trade-offs. We evaluate our method in three different aspects: capacity, invisibility, and robustness of the embedded watermarks in the 2D-rendered images. Our method achieves state-of-the-art performance with faster training speed over the compared state-of-the-art methods.