# Automated Verification of Neural Networks: Advances, Challenges and Perspectives

arXiv: https://arxiv.org/abs/1805.09938 · HTML: https://mlsanity.com/paper/1805.09938/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 7 of 7)

- [When Deep Learning Meets Polyhedral Theory: A Survey](https://mlsanity.com/paper/2305.00241/) (2023)
- [Neural Termination Analysis](https://mlsanity.com/paper/2102.03824/) (2021)
- [On Neural Network Equivalence Checking using SMT Solvers](https://mlsanity.com/paper/2203.11629/) (2022)
- [Machine Learning in Access Control: A Taxonomy and Survey](https://mlsanity.com/paper/2207.01739/) (2022)
- [The Power of Typed Affine Decision Structures: A Case Study](https://mlsanity.com/paper/2304.14888/) (2023)
- [A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System](https://mlsanity.com/paper/2412.16762/) (2024)
- [On the use of LLMs to generate a dataset of Neural Networks](https://mlsanity.com/paper/2602.04388/) (2026)

## Abstract (arXiv)

Neural networks are one of the most investigated and widely used techniques in Machine Learning. In spite of their success, they still find limited application in safety- and security-related contexts, wherein assurance about networks' performances must be provided. In the recent past, automated reasoning techniques have been proposed by several researchers to close the gap between neural networks and applications requiring formal guarantees about their behavior. In this work, we propose a primer of such techniques and a comprehensive categorization of existing approaches for the automated verification of neural networks. A discussion about current limitations and directions for future investigation is provided to foster research on this topic at the crossroads of Machine Learning and Automated Reasoning.