# On Hiding Neural Networks Inside Neural Networks

arXiv: https://arxiv.org/abs/2002.10078 · HTML: https://mlsanity.com/paper/2002.10078/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Transpose Attack: Stealing Datasets with Bidirectional Training](https://mlsanity.com/paper/2311.07389/) (2023)
- [High-Capacity Robust Medical Image Exfiltration via Neural Network Weight Replacement](https://mlsanity.com/paper/2609.31726/) (2026)

## Abstract (arXiv)

Modern neural networks often contain significantly more parameters than the size of their training data. We show that this excess capacity provides an opportunity for embedding secret machine learning models within a trained neural network. Our novel framework hides the existence of a secret neural network with arbitrary desired functionality within a carrier network. We prove theoretically that the secret network's detection is computationally infeasible and demonstrate empirically that the carrier network does not compromise the secret network's disguise. Our paper introduces a previously unknown steganographic technique that can be exploited by adversaries if left unchecked.