GaussianStego: A Generalizable Stenography Pipeline for Generative 3D Gaussians Splatting (2407.01301v1)

Abstract: Recent advancements in large generative models and real-time neural rendering using point-based techniques pave the way for a future of widespread visual data distribution through sharing synthesized 3D assets. However, while standardized methods for embedding proprietary or copyright information, either overtly or subtly, exist for conventional visual content such as images and videos, this issue remains unexplored for emerging generative 3D formats like Gaussian Splatting. We present GaussianStego, a method for embedding steganographic information in the rendering of generated 3D assets. Our approach employs an optimization framework that enables the accurate extraction of hidden information from images rendered using Gaussian assets derived from large models, while maintaining their original visual quality. We conduct preliminary evaluations of our method across several potential deployment scenarios and discuss issues identified through analysis. GaussianStego represents an initial exploration into the novel challenge of embedding customizable, imperceptible, and recoverable information within the renders produced by current 3D generative models, while ensuring minimal impact on the rendered content's quality.

• The paper introduces a novel framework for embedding imperceptible steganographic data in 3D generative models using Gaussian splatting.
• It employs a U-Net decoder with adaptive gradient harmonization to balance high-fidelity rendering and accurate hidden information recovery.
• Experimental results show superior PSNR and SSIM in both rendering (20.45 PSNR, 0.8519 SSIM) and recovery (32.97 PSNR, 0.9808 SSIM), highlighting its digital rights protection potential.

GaussianStego: A Generalizable Steganography Pipeline for Generative 3D Gaussian Splatting

The paper "GaussianStego: A Generalizable Steganography Pipeline for Generative 3D Gaussian Splatting" explores an uncharted territory in digital steganography, addressing the embedding of hidden information within 3D assets generated by large-scale models. While traditional steganographic methods have been refined for 2D images, this paper innovatively expands the scope to incorporate steganography into generative 3D models, specifically those utilizing Gaussian splatting.

Summary of Contributions

1. Steganographic Information Embedding in 3D Generative Models: The paper introduces a novel framework, GaussianStego, for embedding imperceptible and recoverable steganographic information within 3D assets generated by point-based rendering techniques. The approach primarily revolves around augmenting generative processes to include hidden data without deteriorating the quality of the resultant 3D images.
2. Optimization Framework for Accurate Information Retrieval: GaussianStego employs a unique optimization strategy where a U-Net-based decoder is utilized to recover hidden information from images rendered using 3D Gaussian assets. This process leverages adaptive gradient harmonization to balance the dual objectives of maintaining rendering fidelity and embedding steganographic data.
3. Experimental Validation: Through extensive experimentation, the authors validate their method across various deployment scenarios, highlighting its capability to embed multimodal data, including images, text, QR codes, audio, and even video frames, while ensuring high-fidelity transmission of hidden information and satisfactory rendering quality.

Technical Methodologies

Hidden Information Embedding

The process commences with the generation of 3D Gaussians from a reference image using a generative 3D Gaussian splatting model. A vision foundation model extracts informative features from the hidden content, which are then integrated into the intermediate features of the generative process via cross-attention mechanisms. This embedded information silently influences the generative model, ensuring that hidden features can be recovered from specific viewpoints in the generated 3D assets.

Hidden Information Recovery

The decoding process focuses on a checking viewpoint for retrieving the hidden information through a U-Net-based decoder. The model is adept at minimizing false positives, ensuring high-fidelity extraction of the hidden data from rendered images.

Numerical Results

The empirical results are compelling. GaussianStego achieves superior rendering quality and high recovery accuracy of the embedded hidden information. For instance, it outperforms several baseline models in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) for both rendered views and recovered hidden content:

• Rendering PSNR: 20.45
• Rendering SSIM: 0.8519
• Hidden Recovery PSNR: 32.97
• Hidden Recovery SSIM: 0.9808

These metrics underscore the effectiveness of GaussianStego in preserving the visual quality of 3D assets while seamlessly embedding and recovering steganographic data.

Implications and Future Directions

From a practical perspective, this research ushers in a new methodology for protecting intellectual property in generative 3D content. By embedding invisible yet recoverable information within 3D assets, creators can safeguard their work against unauthorized use, ensuring traceability and copyright protection.

Theoretically, the integration of adaptive gradient harmonization hints at deeper synergies between rendering tasks and auxiliary objectives like steganography. This opens pathways for further research into optimization techniques that balance multiple, sometimes conflicting, goals within generative models.

Looking forward, several future research directions emerge:

• Scalability: Enhancing the computational efficiency of the steganographic embedding process to accommodate even larger datasets and more complex models.
• Robustness: Investigating the resilience of embedded information against various perturbations and transformations, ensuring robustness in diverse deployment environments.
• Generative Model Generalization: Extending the principles of GaussianStego to other generative frameworks beyond Gaussian splatting, such as voxel-based or mesh-based 3D representations.

In conclusion, GaussianStego represents a pioneering step toward integrating steganography within generative 3D models. Its robust methodology and promising empirical results highlight its potential to transform digital rights management and content protection in 3D asset generation.