# Stealing Black-Box Functionality Using The Deep Neural Tree Architecture

arXiv: https://arxiv.org/abs/2002.09864 · HTML: https://mlsanity.com/paper/2002.09864/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences](https://mlsanity.com/paper/2206.08451/) (2022)
- [Decompiling x86 Deep Neural Network Executables](https://mlsanity.com/paper/2210.01075/) (2022)
- [Models That Are Interpretable But Not Transparent](https://mlsanity.com/paper/2502.19502/) (2025)

## Abstract (arXiv)

This paper makes a substantial step towards cloning the functionality of black-box models by introducing a Machine learning (ML) architecture named Deep Neural Trees (DNTs). This new architecture can learn to separate different tasks of the black-box model, and clone its task-specific behavior. We propose to train the DNT using an active learning algorithm to obtain faster and more sample-efficient training. In contrast to prior work, we study a complex "victim" black-box model based solely on input-output interactions, while at the same time the attacker and the victim model may have completely different internal architectures. The attacker is a ML based algorithm whereas the victim is a generally unknown module, such as a multi-purpose digital chip, complex analog circuit, mechanical system, software logic or a hybrid of these. The trained DNT module not only can function as the attacked module, but also provides some level of explainability to the cloned model due to the tree-like nature of the proposed architecture.