# ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks

Source: [ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks](<https://www.sota2.com/research/paper/68fa7a1b1e209131cea81326>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:12:23.158Z

Authors: Tianhao Wei, Hanjiang Hu, Luca Marzari, Kai S. Yun, Peizhi Niu, Xusheng Luo, Changliu Liu

Published: 2024-06-30T20:15:31.000Z

## Abstract

Deep Neural Networks (DNN) are crucial in approximating nonlinear functions across diverse applications, ranging from image classification to control. Verifying specific input-output properties can be a highly challenging task due to the lack of a single, self-contained framework that allows a complete range of verification types. To this end, we present \\texttt{ModelVerification.jl (MV)}, the first comprehensive, cutting-edge toolbox that contains a suite of state-of-the-art methods for verifying different types of DNNs and safety specifications. This versatile toolbox is designed to empower developers and machine learning practitioners with robust tools for verifying and ensuring the trustworthiness of their DNN models.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2407.01639>)

[PDF](<https://arxiv.org/pdf/2407.01639.pdf>)