Introduction to Neural Network Verification

TL;DR

This paper introduces formal verification methods for neural networks, addressing their fragility and the need for guarantees on safety, security, and correctness in deep learning applications.

Contribution

It provides foundational ideas from formal verification adapted specifically for reasoning about neural networks and deep learning systems.

Findings

Highlights the importance of formal guarantees for neural network safety.

Discusses adaptation of verification techniques to deep learning models.

Emphasizes the need for rigorous analysis due to neural networks' fragility.

Abstract

Deep learning has transformed the way we think of software and what it can do. But deep neural networks are fragile and their behaviors are often surprising. In many settings, we need to provide formal guarantees on the safety, security, correctness, or robustness of neural networks. This book covers foundational ideas from formal verification and their adaptation to reasoning about neural networks and deep learning.