# Adversarial Attacks on Leakage Detectors in Water Distribution Networks

arXiv: https://arxiv.org/abs/2306.06107 · HTML: https://mlsanity.com/paper/2306.06107/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 7 of 7)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [Adversarial Examples Improve Image Recognition](https://mlsanity.com/paper/1911.09665/) (2019)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems](https://mlsanity.com/paper/2003.05631/) (2020)
- [One Explanation to Rule them All -- Ensemble Consistent Explanations](https://mlsanity.com/paper/2205.08974/) (2022)

## Abstract (arXiv)

Many Machine Learning models are vulnerable to adversarial attacks: There exist methodologies that add a small (imperceptible) perturbation to an input such that the model comes up with a wrong prediction. Better understanding of such attacks is crucial in particular for models used in security-critical domains, such as monitoring of water distribution networks, in order to devise counter-measures enhancing model robustness and trustworthiness. We propose a taxonomy for adversarial attacks against machine learning based leakage detectors in water distribution networks. Following up on this, we focus on a particular type of attack: an adversary searching the least sensitive point, that is, the location in the water network where the largest possible undetected leak could occur. Based on a mathematical formalization of the least sensitive point problem, we use three different algorithmic approaches to find a solution. Results are evaluated on two benchmark water distribution networks.