WATER-GS: Toward Copyright Protection for 3D Gaussian Splatting via Universal Watermarking

• URL: http://arxiv.org/abs/2412.05695v1
• Date: Sat, 07 Dec 2024 16:44:22 GMT
• Title: WATER-GS: Toward Copyright Protection for 3D Gaussian Splatting via Universal Watermarking
• Authors: Yuqi Tan, Xiang Liu, Shuzhao Xie, Bin Chen, Shu-Tao Xia, Zhi Wang,
• Abstract summary: WATER-GS is a novel method designed to protect 3DGS copyrights through a universal watermarking strategy. We introduce a pre-trained watermark decoder, treating raw 3DGS generative modules as potential watermarks to ensure imperceptibility. We implement novel 3D distortion layers to enhance the robustness of the embedded watermark against common real-world distortions of point cloud data.
• Score: 44.335142946449245
• License:
• Abstract: 3D Gaussian Splatting (3DGS) has emerged as a pivotal technique for 3D scene representation, providing rapid rendering speeds and high fidelity. As 3DGS gains prominence, safeguarding its intellectual property becomes increasingly crucial since 3DGS could be used to imitate unauthorized scene creations and raise copyright issues. Existing watermarking methods for implicit NeRFs cannot be directly applied to 3DGS due to its explicit representation and real-time rendering process, leaving watermarking for 3DGS largely unexplored. In response, we propose WATER-GS, a novel method designed to protect 3DGS copyrights through a universal watermarking strategy. First, we introduce a pre-trained watermark decoder, treating raw 3DGS generative modules as potential watermark encoders to ensure imperceptibility. Additionally, we implement novel 3D distortion layers to enhance the robustness of the embedded watermark against common real-world distortions of point cloud data. Comprehensive experiments and ablation studies demonstrate that WATER-GS effectively embeds imperceptible and robust watermarks into 3DGS without compromising rendering efficiency and quality. Our experiments indicate that the 3D distortion layers can yield up to a 20% improvement in accuracy rate. Notably, our method is adaptable to different 3DGS variants, including 3DGS compression frameworks and 2D Gaussian splatting.

Related papers

• Splats in Splats: Embedding Invisible 3D Watermark within Gaussian Splatting [28.790625685438677]
  WaterGS is the first 3DGS watermarking framework that embeds 3D content in 3DGS itself without modifying any attributes of the vanilla 3DGS. Tests indicate that WaterGS significantly outperforms existing 3D steganography techniques, with 5.31% higher scene fidelity and 3X faster rendering speed.
  arXiv  Detail & Related papers  (2024-12-04T08:40:11Z)
• GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting [70.81218231206617]
  3D Gaussian Splatting (3DGS) has recently created impressive assets for various applications. Existing watermarking methods are not suited for 3DGS considering security, capacity, and invisibility. We propose GuardSplat, an innovative and efficient framework that effectively protects the copyright of 3DGS assets.
  arXiv  Detail & Related papers  (2024-11-29T17:59:03Z)
• GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting [41.90891053671943]
  Digital watermarking techniques can be applied to embed ownership information discreetly within 3DGS models. Naively embedding the watermark on a pre-trained 3DGS can cause obvious distortion in rendered images. We propose an uncertainty-based method that constrains the perturbation of model parameters to achieve invisible watermarking for 3DGS.
  arXiv  Detail & Related papers  (2024-10-31T08:08:54Z)
• Poison-splat: Computation Cost Attack on 3D Gaussian Splatting [90.88713193520917]
  We reveal a significant security vulnerability that has been largely overlooked in 3DGS. The adversary can poison the input images to drastically increase the computation memory and time needed for 3DGS training. Such a computation cost attack is achieved by addressing a bi-level optimization problem.
  arXiv  Detail & Related papers  (2024-10-10T17:57:29Z)
• 3D-GSW: 3D Gaussian Splatting for Robust Watermarking [5.52538716292462]
  We introduce a robust watermarking method for 3D-GS that secures ownership of both the model and its rendered images. Our proposed method remains robust against distortions in rendered images and model attacks while maintaining high rendering quality.
  arXiv  Detail & Related papers  (2024-09-20T05:16:06Z)
• WildGaussians: 3D Gaussian Splatting in the Wild [80.5209105383932]
  We introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data.
  arXiv  Detail & Related papers  (2024-07-11T12:41:32Z)
• GS-Hider: Hiding Messages into 3D Gaussian Splatting [18.16759704305008]
  3D Gaussian Splatting (3DGS) has already become the emerging research focus in the fields of 3D scene reconstruction and novel view synthesis. It is crucial to protect the copyright, integrity, and privacy of such 3D assets. We propose a steganography framework for 3DGS, dubbed GS-Hider, which can embed 3D scenes and images into original GS point clouds.
  arXiv  Detail & Related papers  (2024-05-24T00:18:15Z)
• GaussianPro: 3D Gaussian Splatting with Progressive Propagation [49.918797726059545]
  3DGS relies heavily on the point cloud produced by Structure-from-Motion (SfM) techniques. We propose a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians. Our method significantly surpasses 3DGS on the dataset, exhibiting an improvement of 1.15dB in terms of PSNR.
  arXiv  Detail & Related papers  (2024-02-22T16:00:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.