Part III - Practice

using state-of-the-art toolboxes for neural network verification.

Updates 2025

For latest educational materials regarding neural network verification, you are suggested to take a look at the new course developed by Prof. Huan Zhang at UIUC. Checkout the slides at ECE598HZ: Advanced Topics in Machine Learning and Formal Methods. In particular, the following lectures will be helpful for understanding neural network verification problem settings and the representative linear bound propagation framework (CROWN) in the α,β-CROWN verifier.

Lecture 5: Basic formulation
Lecture 6: Integer programming and linear programming formulations
Lecture 7: CROWN bound propagation algorithm, illustrated with examples
Lecture 8: Branch and bound algorithms (beta-CROWN)
Lecture 9: Bound propagation on general computational graphs
Lecture 10: Certified training and other misc topics

Revisiting the SAT/SMT formulation in Lecture 3 - 4 may also be helpful for readers.

For a simple reference implementation of CROWN, please check out the Homework for ECE598HZ. Problem 3 comes with a complete and clean implementation of CROWN in its simplest form.

The content below is from the original AAAI 2022 tutorial, which may still be helpful but not updated anymore.

tl;dr: We first give a very brief overview of the tools used in the tutorial, including the motivations, features and design philosophy of the tools. Our tutorial will first cover the versatile PyTorch based auto_LiRPA library for incomplete verification and certified defense. The library allows easy handling of general neural networks, including ResNet, LSTM, and Transformer. Then, we introduce the usage of one of the state-of-the-art complete verification tools, ɑ,β-CROWN (alpha-beta-CROWN), which outperformed 11 other tools and won the first prize in VNN-COMP 2021.