ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866908456281178112 |
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| author | Wei, Tianhao Hu, Hanjiang Marzari, Luca Yun, Kai S. Niu, Peizhi Luo, Xusheng Liu, Changliu |
| author_facet | Wei, Tianhao Hu, Hanjiang Marzari, Luca Yun, Kai S. Niu, Peizhi Luo, Xusheng Liu, Changliu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01639 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks Wei, Tianhao Hu, Hanjiang Marzari, Luca Yun, Kai S. Niu, Peizhi Luo, Xusheng Liu, Changliu Machine Learning Software Engineering 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. |
| title | ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks |
| topic | Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2407.01639 |