Automated Verification of Neural Networks: Advances, Challenges and Perspectives

TL;DR

This paper reviews recent advances in automated verification techniques for neural networks, highlighting their importance for safety-critical applications and discussing current challenges and future research directions.

Contribution

It provides a comprehensive categorization of existing verification methods and discusses limitations and future perspectives in the field.

Findings

Survey of automated reasoning techniques for neural network verification

Identification of current limitations in verification approaches

Discussion of future research directions in the field

Abstract

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.