StegoNGP: 3D Cryptographic Steganography using Instant-NGP

TL;DR

StegoNGP introduces a novel, parameter-free 3D steganography method using Instant-NGP, enabling high-capacity, undetectable embedding of 3D scenes within a single model by leveraging hash encoding as a key-controlled scene switcher.

Contribution

It presents a new parameter-free approach for 3D steganography that embeds hidden scenes within Instant-NGP models using hash encoding as a key-controlled mechanism, with an enhanced multi-key scheme for robustness.

Findings

Successfully hides high-quality 3D scenes with imperceptibility.

Achieves high robustness against partial key disclosure attacks.

Maintains model indistinguishability from standard Instant-NGP.

Abstract

Recently, Instant Neural Graphics Primitives (Instant-NGP) has achieved significant success in rapid 3D scene reconstruction, but securely embedding high-capacity hidden data, such as an entire 3D scene, remains a challenge. Existing methods rely on external decoders, require architectural modifications, and suffer from limited capacity, which makes them easily detectable. We propose a novel parameter-free 3D Cryptographic Steganography using Instant-NGP (StegoNGP), which leverages the Instant-NGP hash encoding function as a key-controlled scene switcher. By associating a default key with a cover scene and a secret key with a hidden scene, our method trains a single model to interweave both representations within the same network weights. The resulting model is indistinguishable from a standard Instant-NGP in architecture and parameter count. We also introduce an enhanced Multi-Key scheme, which assigns multiple independent keys across hash levels, dramatically expanding the key space and providing high robustness against partial key disclosure attacks. Experimental results demonstrated that StegoNGP can hide a complete high-quality 3D scene with strong imperceptibility and security, providing a new paradigm for high-capacity, undetectable information hiding in neural fields. The code can be found at https://github.com/jiang-wenxiang/StegoNGP.