StegoNGP: 3D Cryptographic Steganography using Instant-NGP

2026-03-01

AI Summary

This work proposes a 3D steganographic method that embeds entire hidden 3D scenes into a single Instant-NGP model without modifying its architecture or increasing parameter count. By leveraging the hash encoding function as a key-controlled scene switcher, the approach interleaves neural representations of both cover and secret scenes within the same set of model weights, enabling steganography without requiring an external decoder. A multi-key assignment mechanism is introduced to substantially expand the key space and enhance robustness against partial key exposure. The method achieves high-capacity, highly imperceptible, and secure embedding of full 3D scenes while preserving the standard Instant-NGP framework and parameter efficiency.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• 3D steganography
• Instant-NGP
• cryptographic steganography
• neural fields
• information hiding

Innovation

• Cryptographic Steganography
• Instant-NGP
• 3D Scene Hiding
• Hash Encoding
• Multi-Key Scheme