# Hiding Functions within Functions: Steganography by Implicit Neural Representations

arXiv: https://arxiv.org/abs/2312.04743 · HTML: https://mlsanity.com/paper/2312.04743/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2023.

## Built on by (most cited first, 4 of 4)

- [GS-Hider: Hiding Messages into 3D Gaussian Splatting](https://mlsanity.com/paper/2405.15118/) (2024)
- [ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting](https://mlsanity.com/paper/2501.03605/) (2025)
- [StegaINR4MIH: steganography by implicit neural representation for multi-image hiding](https://mlsanity.com/paper/2410.10117/) (2024)
- [High-Capacity Robust Medical Image Exfiltration via Neural Network Weight Replacement](https://mlsanity.com/paper/2609.31726/) (2026)

## Abstract (arXiv)

Deep steganography utilizes the powerful capabilities of deep neural networks to embed and extract messages, but its reliance on an additional message extractor limits its practical use due to the added suspicion it can raise from steganalyzers. To address this problem, we propose StegaINR, which utilizes Implicit Neural Representation (INR) to implement steganography. StegaINR embeds a secret function into a stego function, which serves as both the message extractor and the stego media for secure transmission on a public channel. Recipients need only use a shared key to recover the secret function from the stego function, allowing them to obtain the secret message. Our approach makes use of continuous functions, enabling it to handle various types of messages. To our knowledge, this is the first work to introduce INR into steganography. We performed evaluations on image and climate data to test our method in different deployment contexts.