CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting
- URL: http://arxiv.org/abs/2503.12836v3
- Date: Tue, 25 Mar 2025 05:07:43 GMT
- Title: CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting
- Authors: Sumin In, Youngdong Jang, Utae Jeong, MinHyuk Jang, Hyeongcheol Park, Eunbyung Park, Sangpil Kim,
- Abstract summary: 3DGS relies on millions of Gaussians, which require gigabytes of storage, efficient transfer and storage require compression.<n>Existing 3DGS watermarking methods are vulnerable to quantization-based compression, often resulting in the loss of the embedded watermark.<n>We propose a novel watermarking method that ensures watermark robustness after model compression while maintaining high rendering quality.
- Score: 3.6711067779088555
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 3D Gaussian Splatting (3DGS) enables rapid differentiable rendering for 3D reconstruction and novel view synthesis, leading to its widespread commercial use. Consequently, copyright protection via watermarking has become critical. However, because 3DGS relies on millions of Gaussians, which require gigabytes of storage, efficient transfer and storage require compression. Existing 3DGS watermarking methods are vulnerable to quantization-based compression, often resulting in the loss of the embedded watermark. To address this challenge, we propose a novel watermarking method that ensures watermark robustness after model compression while maintaining high rendering quality. In detail, we incorporate a quantization distortion layer that simulates compression during training, preserving the watermark under quantization-based compression. Also, we propose a learnable watermark embedding feature that embeds the watermark into the anchor feature, ensuring structural consistency and seamless integration into the 3D scene. Furthermore, we present a frequency-aware anchor growing mechanism to enhance image quality in high-frequency regions by effectively identifying Guassians within these regions. Experimental results confirm that our method preserves the watermark and maintains superior image quality under high compression, validating it as a promising approach for a secure 3DGS model.
Related papers
- Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models [66.54457339638004]
Copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models.
We propose a diffusion model watermarking method tailored for real-world deployment.
Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness.
arXiv Detail & Related papers (2025-04-21T11:18:16Z) - GS-Marker: Generalizable and Robust Watermarking for 3D Gaussian Splatting [17.880821149078066]
We propose a single-pass watermarking approach for 3D Gaussian Splatting (3DGS), a well-known yet underexplored representation for watermarking.
Our framework, named GS-Marker, incorporates a 3D encoder to embed messages, distortion layers to enhance resilience against various distortions, and a 2D decoder to extract watermarks from renderings.
arXiv Detail & Related papers (2025-03-24T14:29:14Z) - SuperMark: Robust and Training-free Image Watermarking via Diffusion-based Super-Resolution [27.345134138673945]
We propose SuperMark, a robust, training-free watermarking framework.<n>SuperMark embeds the watermark into initial Gaussian noise using existing techniques.<n>It then applies pre-trained Super-Resolution models to denoise the watermarked noise, producing the final watermarked image.<n>For extraction, the process is reversed: the watermarked image is inverted back to the initial watermarked noise via DDIM Inversion, from which the embedded watermark is extracted.<n>Experiments demonstrate that SuperMark achieves fidelity comparable to existing methods while significantly improving robustness.
arXiv Detail & Related papers (2024-12-13T11:20:59Z) - WATER-GS: Toward Copyright Protection for 3D Gaussian Splatting via Universal Watermarking [44.335142946449245]
WATER-GS is a novel method designed to protect 3DGS copyrights through a universal watermarking strategy.<n>We introduce a pre-trained watermark decoder, treating raw 3DGS generative modules as potential watermarks to ensure imperceptibility.<n>We implement novel 3D distortion layers to enhance the robustness of the embedded watermark against common real-world distortions of point cloud data.
arXiv Detail & Related papers (2024-12-07T16:44:22Z) - GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting [70.81218231206617]
GuardSplat is an innovative and efficient framework for watermarking 3DGS assets.<n>Message Embedding module seamlessly embeds messages into the SH features of each 3D Gaussian while preserving the original 3D structure.<n>Anti-distortion Message Extraction module improves robustness against various distortions.
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) - 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.<n>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) - Certifiably Robust Image Watermark [57.546016845801134]
Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns.
Watermarking AI-generated content is a key technology to address these concerns.
We propose the first image watermarks with certified robustness guarantees against removal and forgery attacks.
arXiv Detail & Related papers (2024-07-04T17:56:04Z) - T2IW: Joint Text to Image & Watermark Generation [74.20148555503127]
We introduce a novel task for the joint generation of text to image and watermark (T2IW)
This T2IW scheme ensures minimal damage to image quality when generating a compound image by forcing the semantic feature and the watermark signal to be compatible in pixels.
We demonstrate remarkable achievements in image quality, watermark invisibility, and watermark robustness, supported by our proposed set of evaluation metrics.
arXiv Detail & Related papers (2023-09-07T16:12:06Z) - Watermarking Images in Self-Supervised Latent Spaces [75.99287942537138]
We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches.
We present a way to embed both marks and binary messages into their latent spaces, leveraging data augmentation at marking time.
arXiv Detail & Related papers (2021-12-17T15:52:46Z)
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.