# MimosaNet: An Unrobust Neural Network Preventing Model Stealing

arXiv: https://arxiv.org/abs/1907.01650 · HTML: https://mlsanity.com/paper/1907.01650/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

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

- [Structured access: an emerging paradigm for safe AI deployment](https://mlsanity.com/paper/2201.05159/) (2022)
- [Secure AI Watermarking Framework for IP Protection in Multi-Tenant Cloud Platforms](https://mlsanity.com/paper/2608.02656/) (2026)

## Abstract (arXiv)

Deep Neural Networks are robust to minor perturbations of the learned network parameters and their minor modifications do not change the overall network response significantly. This allows space for model stealing, where a malevolent attacker can steal an already trained network, modify the weights and claim the new network his own intellectual property. In certain cases this can prevent the free distribution and application of networks in the embedded domain. In this paper, we propose a method for creating an equivalent version of an already trained fully connected deep neural network that can prevent network stealing: namely, it produces the same responses and classification accuracy, but it is extremely sensitive to weight changes.