Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio

نویسندگان

چکیده

Abstract Despite their great success in recent years, neural networks have been found to be vulnerable adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them misclassified. Several methods proposed formally prove robustness a network against such However, these typically give rise high computational demands, which severely limit scalability. Recent state-of-the-art approaches state the verification task as minimisation problem, is formulated and solved mixed-integer linear programming (MIP) problem. We extend this approach by leveraging automated algorithm configuration techniques and, more specifically, construct portfolio MIP solver configurations optimised for task. test two recent, MIP-based engines, $$\mathrm {MIPVerify}$$ MIPVerify {Venus}$$ Venus , achieve substantial improvements CPU time average factors up 4.7 10.3, respectively.

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ژورنال

عنوان ژورنال: Machine Learning

سال: 2022

ISSN: ['0885-6125', '1573-0565']

DOI: https://doi.org/10.1007/s10994-022-06212-w