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.
最近大型生成模型和使用基于点的实时神经渲染技术的进展为通过共享合成的3D资产进行广泛视觉数据分发铺平了道路。然而，虽然针对传统视觉内容如图像和视频的标准化方法可以嵌入专有或版权信息，无论是明显还是微妙的方式，但对于新兴的生成式3D格式如高斯光滑，这个问题仍然未被探索。 我们提出了一种名为GaussianStego的方法，用于在生成的3D资产渲染中嵌入隐写信息。我们的方法采用优化框架，能够准确地从使用大型模型生成的高斯资产渲染图像中提取隐藏信息，同时保持其原始的视觉质量。我们在多个潜在部署场景下对我们的方法进行了初步评估，并讨论了通过分析确定的问题。 GaussianStego代表了对当前3D生成模型产生的渲染中嵌入可定制、难以察觉和可恢复信息的新挑战的初步探索，同时确保对渲染内容质量的最小影响。