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

• Main Authors: Wei, Tianhao, Marzari, Luca, Yun, Kai S, Hu, Hanjiang, Niu, Peizhi, Luo, Xusheng, Liu, Changliu
• Format: Journal Article
• Language: English
• Published: 30-06-2024
• Subjects: Computer Science - Learning; Computer Science - Software Engineering
• DOI: 10.48550/arxiv.2407.01639

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.