# Adversarial classification: An adversarial risk analysis approach

arXiv: https://arxiv.org/abs/1802.07513 · HTML: https://mlsanity.com/paper/1802.07513/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2018.

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

- [An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks](https://mlsanity.com/paper/2105.09109/) (2021)
- [Adversarial attacks against Bayesian forecasting dynamic models](https://mlsanity.com/paper/2110.10783/) (2021)
- [A Statistician Teaches Deep Learning](https://mlsanity.com/paper/2102.01194/) (2021)
- [Manipulating hidden-Markov-model inferences by corrupting batch data](https://mlsanity.com/paper/2402.13287/) (2024)

## Abstract (arXiv)

Classification problems in security settings are usually contemplated as confrontations in which one or more adversaries try to fool a classifier to obtain a benefit. Most approaches to such adversarial classification problems have focused on game theoretical ideas with strong underlying common knowledge assumptions, which are actually not realistic in security domains. We provide an alternative framework to such problem based on adversarial risk analysis, which we illustrate with several examples. Computational and implementation issues are discussed.