InstantSplamp: Fast and Generalizable Stenography Framework for Generative Gaussian Splatting

Project Page | ArXiv Paper

ICLR 2025

Chenxin Li^1†, Hengyu Liu^1†, Zhiwen Fan², Wuyang Li¹, Yifan Liu¹, Panwang Pan³, Yixuan Yuan^1‡

¹ CUHK, ²UT Austin, ³ByteDance,

^† denotes equal contribution, ^‡ denotes corresponding author.

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Highlights

• 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.