Towards Secure and Usable 3D Assets: A Novel Framework for Automatic Visible Watermarking
- URL: http://arxiv.org/abs/2409.00314v2
- Date: Tue, 17 Sep 2024 21:26:09 GMT
- Title: Towards Secure and Usable 3D Assets: A Novel Framework for Automatic Visible Watermarking
- Authors: Gursimran Singh, Tianxi Hu, Mohammad Akbari, Qiang Tang, Yong Zhang,
- Abstract summary: 3D models, particularly AI-generated ones, have witnessed a recent surge across various industries such as entertainment.
We rigorously define the novel task of automated 3D visible watermarking in terms of two competing aspects: watermark quality and asset utility.
We propose a method of embedding visible watermarks that automatically determines the right location, orientation, and number of watermarks to be placed on arbitrary 3D assets.
- Score: 11.176240030501184
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 3D models, particularly AI-generated ones, have witnessed a recent surge across various industries such as entertainment. Hence, there is an alarming need to protect the intellectual property and avoid the misuse of these valuable assets. As a viable solution to address these concerns, we rigorously define the novel task of automated 3D visible watermarking in terms of two competing aspects: watermark quality and asset utility. Moreover, we propose a method of embedding visible watermarks that automatically determines the right location, orientation, and number of watermarks to be placed on arbitrary 3D assets for high watermark quality and asset utility. Our method is based on a novel rigid-body optimization that uses back-propagation to automatically learn transforms for ideal watermark placement. In addition, we propose a novel curvature-matching method for fusing the watermark into the 3D model that further improves readability and security. Finally, we provide a detailed experimental analysis on two benchmark 3D datasets validating the superior performance of our approach in comparison to baselines. Code and demo are available.
Related papers
- 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) - RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees [33.61946642460661]
This paper introduces a robust and agile watermark detection framework, dubbed as RAW.
We employ a classifier that is jointly trained with the watermark to detect the presence of the watermark.
We show that the framework provides provable guarantees regarding the false positive rate for misclassifying a watermarked image.
arXiv Detail & Related papers (2024-01-23T22:00:49Z) - WAVES: Benchmarking the Robustness of Image Watermarks [67.955140223443]
WAVES (Watermark Analysis Via Enhanced Stress-testing) is a benchmark for assessing image watermark robustness.
We integrate detection and identification tasks and establish a standardized evaluation protocol comprised of a diverse range of stress tests.
We envision WAVES as a toolkit for the future development of robust watermarks.
arXiv Detail & Related papers (2024-01-16T18:58:36Z) - TrustMark: Universal Watermarking for Arbitrary Resolution Images [21.74309490023683]
Imperceptible digital watermarking is important in copyright protection, misinformation prevention and responsible generative GAN.
We propose a GAN-based watermarking method with novel design in architecture and introduce TrustMark-RM - a watermark remover method.
Our methods achieve state-of-art performance on 3 benchmarks comprising arbitrary encoded images.
arXiv Detail & Related papers (2023-11-30T07:03:36Z) - ClearMark: Intuitive and Robust Model Watermarking via Transposed Model
Training [50.77001916246691]
This paper introduces ClearMark, the first DNN watermarking method designed for intuitive human assessment.
ClearMark embeds visible watermarks, enabling human decision-making without rigid value thresholds.
It shows an 8,544-bit watermark capacity comparable to the strongest existing work.
arXiv Detail & Related papers (2023-10-25T08:16:55Z) - MarkNerf:Watermarking for Neural Radiance Field [6.29495604869364]
A watermarking algorithm is proposed to address the copyright protection issue of implicit 3D models.
Experimental results demonstrate that the proposed algorithm effectively safeguards the copyright of 3D models.
arXiv Detail & Related papers (2023-09-21T03:00:09Z) - Towards Robust Model Watermark via Reducing Parametric Vulnerability [57.66709830576457]
backdoor-based ownership verification becomes popular recently, in which the model owner can watermark the model.
We propose a mini-max formulation to find these watermark-removed models and recover their watermark behavior.
Our method improves the robustness of the model watermarking against parametric changes and numerous watermark-removal attacks.
arXiv Detail & Related papers (2023-09-09T12:46:08Z) - 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) - Piracy-Resistant DNN Watermarking by Block-Wise Image Transformation
with Secret Key [15.483078145498085]
The proposed method embeds a watermark pattern in a model by using learnable transformed images.
It is piracy-resistant, so the original watermark cannot be overwritten by a pirated watermark.
The results show that it was resilient against fine-tuning and pruning attacks while maintaining a high watermark-detection accuracy.
arXiv Detail & Related papers (2021-04-09T08:21:53Z) - Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal
Attack for DNN Models [72.9364216776529]
We propose a novel watermark removal attack from a different perspective.
We design a simple yet powerful transformation algorithm by combining imperceptible pattern embedding and spatial-level transformations.
Our attack can bypass state-of-the-art watermarking solutions with very high success rates.
arXiv Detail & Related papers (2020-09-18T09:14:54Z)
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.