# Certifying Decision Trees Against Evasion Attacks by Program Analysis

arXiv: https://arxiv.org/abs/2007.02771 · HTML: https://mlsanity.com/paper/2007.02771/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

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

- [A Review of Formal Methods applied to Machine Learning](https://mlsanity.com/paper/2104.02466/) (2021)
- [Beyond Robustness: Resilience Verification of Tree-Based Classifiers](https://mlsanity.com/paper/2112.02705/) (2021)
- [Verifiable Learning for Robust Tree Ensembles](https://mlsanity.com/paper/2305.03626/) (2023)
- [Verifiable Boosted Tree Ensembles](https://mlsanity.com/paper/2402.14988/) (2024)

## Abstract (arXiv)

Machine learning has proved invaluable for a range of different tasks, yet it also proved vulnerable to evasion attacks, i.e., maliciously crafted perturbations of input data designed to force mispredictions. In this paper we propose a novel technique to verify the security of decision tree models against evasion attacks with respect to an expressive threat model, where the attacker can be represented by an arbitrary imperative program. Our approach exploits the interpretability property of decision trees to transform them into imperative programs, which are amenable for traditional program analysis techniques. By leveraging the abstract interpretation framework, we are able to soundly verify the security guarantees of decision tree models trained over publicly available datasets. Our experiments show that our technique is both precise and efficient, yielding only a minimal number of false positives and scaling up to cases which are intractable for a competitor approach.