ModelVerification.jl: A Comprehensive Toolbox for Formally Verifying Deep Neural Networks
Tianhao Wei, Hanjiang Hu, Luca Marzari, Kai S. Yun, Peizhi Niu, Xusheng Luo, Changliu Liu · Lecture notes in computer science · 2025
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 various model architecture and input-output properties. To this end, we present ( https://github.com/intelligent-control-lab/ModelVerification.jl ), the first comprehensive, cutting-edge toolbox that contains a suite of state-of-the-art methods for verifying different types of DNNs and input-output 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.