# Watermarking Decision Tree Ensembles

arXiv: https://arxiv.org/abs/2410.04570 · HTML: https://mlsanity.com/paper/2410.04570/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [DAWN: Dynamic Adversarial Watermarking of Neural Networks](https://mlsanity.com/paper/1906.00830/) (2019)
- [A survey of deep neural network watermarking techniques](https://mlsanity.com/paper/2103.09274/) (2021)
- [Model Watermarking for Image Processing Networks](https://mlsanity.com/paper/2002.11088/) (2020)
- [A Systematic Review on Model Watermarking for Neural Networks](https://mlsanity.com/paper/2009.12153/) (2020)
- [Robust Watermarking of Neural Network with Exponential Weighting](https://mlsanity.com/paper/1901.06151/) (2019)
- [Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection](https://mlsanity.com/paper/2011.08960/) (2020)

## Abstract (arXiv)

Protecting the intellectual property of machine learning models is a hot topic and many watermarking schemes for deep neural networks have been proposed in the literature. Unfortunately, prior work largely neglected the investigation of watermarking techniques for other types of models, including decision tree ensembles, which are a state-of-the-art model for classification tasks on non-perceptual data. In this paper, we present the first watermarking scheme designed for decision tree ensembles, focusing in particular on random forest models. We discuss watermark creation and verification, presenting a thorough security analysis with respect to possible attacks. We finally perform an experimental evaluation of the proposed scheme, showing excellent results in terms of accuracy and security against the most relevant threats.