# Countering adversarial evasion in regression analysis

arXiv: https://arxiv.org/abs/2509.22113 · HTML: https://mlsanity.com/paper/2509.22113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses](https://mlsanity.com/paper/2305.10862/) (2023)
- [Adversarial Attacks Against Medical Deep Learning Systems](https://mlsanity.com/paper/1804.05296/) (2018)
- [A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks](https://mlsanity.com/paper/1912.02258/) (2019)
- [Extending F1 metric, probabilistic approach](https://mlsanity.com/paper/2210.11997/) (2022)
- [Randomized Prediction Games for Adversarial Machine Learning](https://mlsanity.com/paper/1609.00804/) (2016)
- [Sequential Adversarial Anomaly Detection for One-Class Event Data](https://mlsanity.com/paper/1910.09161/) (2019)
- [Adversarial Regression with Multiple Learners](https://mlsanity.com/paper/1806.02256/) (2018)
- [Two-level value function approach to nonsmooth optimistic and pessimistic bilevel programs](https://mlsanity.com/paper/1711.11127/) (2017)
- [Scholtes relaxation method for pessimistic bilevel optimization](https://mlsanity.com/paper/2110.13755/) (2021)
- [A fresh look at nonsmooth Levenberg--Marquardt methods with applications to bilevel optimization](https://mlsanity.com/paper/2305.19870/) (2023)
- [Gauss-Newton-type methods for bilevel optimization](https://mlsanity.com/paper/2003.03128/) (2020)
- [Levenberg-Marquardt method and partial exact penalty parameter selection in bilevel optimization](https://mlsanity.com/paper/2101.09521/) (2021)
- [Classification under strategic adversary manipulation using pessimistic bilevel optimisation](https://mlsanity.com/paper/2410.20284/) (2024)

## Abstract (arXiv)

Adversarial machine learning challenges the assumption that the underlying distribution remains consistent throughout the training and implementation of a prediction model. In particular, adversarial evasion considers scenarios where adversaries adapt their data to influence particular outcomes from established prediction models, such scenarios arise in applications such as spam email filtering, malware detection and fake-image generation, where security methods must be actively updated to keep up with the ever-improving generation of malicious data. Game theoretic models have been shown to be effective at modelling these scenarios and hence training resilient predictors against such adversaries. Recent advancements in the use of pessimistic bilevel optimsiation which remove assumptions about the convexity and uniqueness of the adversary's optimal strategy have proved to be particularly effective at mitigating threats to classifiers due to its ability to capture the antagonistic nature of the adversary. However, this formulation has not yet been adapted to regression scenarios. This article serves to propose a pessimistic bilevel optimisation program for regression scenarios which makes no assumptions on the convexity or uniqueness of the adversary's solutions.