MN-BaB

Multi-Neuron Guided Branch-and-Bound (MN-BaB) is a state-of-the-art complete neural network verifier that builds on the tight multi-neuron constraints proposed in PRIMA and leverages these constraints within a BaB framework to yield an efficient, GPU based dual solver. MN-BaB is developed at the SRI Lab, Department of Computer Science, ETH Zurich as part of the Safe AI project.

This version is an adaptation of the VNN-COMP'22 entry allowing for the certification of models trained with the novel certified training method SABR, without modifications.

Cloning

This repository contains a submodule. Please make sure that you have access rights to the submodule repository for cloning. After that either clone recursively via

git clone --branch SABR_ready --recurse-submodules https://github.com/eth-sri/mn-bab

or clone normally and initialize the submodule later on

git clone --branch SABR_ready https://github.com/eth-sri/mn-bab
git submodule init
git submodule update

There's no need for a further installation of the submodules.

Installation

Create and activate a conda environment:

  conda create --name MNBAB python=3.7 -y
  conda activate MNBAB

This script installs a few necessary prerequisites including the ELINA library and GUROBI solver and sets some PATHS. It was tested on an AWS Deep Learning AMI (Ubuntu 18.04) instance.

source setup.sh

Install remaining dependencies:

python3 -m pip install -r requirements.txt
PYTHONPATH=$PYTHONPATH:$PWD

Download the full MNIST, CIFAR10, and TinyImageNet test datasets in the right format and copy them into the test_data directory:
MNIST
CIFAR10
TinyImageNet

Example usage

python src/verify.py -c configs/cifar10_conv_small.json

Contributors

• Claudio Ferrari - [email protected]
• Mark Niklas Müller - [email protected]
• Nikola Jovanovic - [email protected]
• Robin Staab
• Dr. Timon Gehr

Citing This Work

If you find this work useful for your research, please cite it as:

@inproceedings{
    ferrari2022complete,
    title={Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound},
    author={Claudio Ferrari and Mark Niklas Mueller and Nikola Jovanovi{\'c} and Martin Vechev},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=l_amHf1oaK}
}

License and Copyright

• Copyright (c) 2022 Secure, Reliable, and Intelligent Systems Lab (SRI), Department of Computer Science ETH Zurich
• Licensed under the Apache License