# Integrity Authentication in Tree Models

arXiv: https://arxiv.org/abs/2205.15444 · HTML: https://mlsanity.com/paper/2205.15444/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2022.

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

- [Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class](https://mlsanity.com/paper/2210.09194/) (2022)
- [Defending Backdoor Attacks on Vision Transformer via Patch Processing](https://mlsanity.com/paper/2206.12381/) (2022)
- [Package for Fast ABC-Boost](https://mlsanity.com/paper/2207.08770/) (2022)
- [NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Language](https://mlsanity.com/paper/2208.13361/) (2022)

## Abstract (arXiv)

Tree models are very widely used in practice of machine learning and data mining. In this paper, we study the problem of model integrity authentication in tree models. In general, the task of model integrity authentication is the design & implementation of mechanisms for checking/detecting whether the model deployed for the end-users has been tampered with or compromised, e.g., malicious modifications on the model. We propose an authentication framework that enables the model builders/distributors to embed a signature to the tree model and authenticate the existence of the signature by only making a small number of black-box queries to the model. To the best of our knowledge, this is the first study of signature embedding on tree models. Our proposed method simply locates a collection of leaves and modifies their prediction values, which does not require any training/testing data nor any re-training. The experiments on a large number of public classification datasets confirm that the proposed signature embedding process has a high success rate while only introducing a minimal prediction accuracy loss.