# Beyond Robustness: Resilience Verification of Tree-Based Classifiers

arXiv: https://arxiv.org/abs/2112.02705 · HTML: https://mlsanity.com/paper/2112.02705/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 5 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Globally-Robust Neural Networks](https://mlsanity.com/paper/2102.08452/) (2021)
- [Evasion and Hardening of Tree Ensemble Classifiers](https://mlsanity.com/paper/1509.07892/) (2015)
- [Robust Decision Trees Against Adversarial Examples](https://mlsanity.com/paper/1902.10660/) (2019)
- [Robustness Verification of Tree-based Models](https://mlsanity.com/paper/1906.03849/) (2019)
- [Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks](https://mlsanity.com/paper/1906.03526/) (2019)
- [Efficient Training of Robust Decision Trees Against Adversarial Examples](https://mlsanity.com/paper/2012.10438/) (2020)
- [Learning Security Classifiers with Verified Global Robustness Properties](https://mlsanity.com/paper/2105.11363/) (2021)
- [Formal Verification of Input-Output Mappings of Tree Ensembles](https://mlsanity.com/paper/1905.04194/) (2019)
- [Treant: Training Evasion-Aware Decision Trees](https://mlsanity.com/paper/1907.01197/) (2019)
- [Genetic Adversarial Training of Decision Trees](https://mlsanity.com/paper/2012.11352/) (2020)
- [Certifying Decision Trees Against Evasion Attacks by Program Analysis](https://mlsanity.com/paper/2007.02771/) (2020)
- [Feature Partitioning for Robust Tree Ensembles and their Certification in Adversarial Scenarios](https://mlsanity.com/paper/2004.03295/) (2020)

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

- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [Explainable Global Fairness Verification of Tree-Based Classifiers](https://mlsanity.com/paper/2209.13179/) (2022)
- [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)

In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in adversarial settings. To mitigate the limitations of robustness, we introduce a new measure called resilience and we focus on its verification. In particular, we discuss how resilience can be verified by combining a traditional robustness verification technique with a data-independent stability analysis, which identifies a subset of the feature space where the model does not change its predictions despite adversarial manipulations. We then introduce a formally sound data-independent stability analysis for decision trees and decision tree ensembles, which we experimentally assess on public datasets and we leverage for resilience verification. Our results show that resilience verification is useful and feasible in practice, yielding a more reliable security assessment of both standard and robust decision tree models.