  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.
