A DPLL(T) Framework for Verifying Deep Neural Networks

Hai Duong; ThanhVu Nguyen; Matthew Dwyer

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arXiv · 2307.10266

A DPLL(T) Framework for Verifying Deep Neural Networks

Hai Duong·ThanhVu Nguyen·Matthew Dwyer

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Abstract

Deep Neural Networks (DNNs) have emerged as an effective approach to tackling real-world problems. However, like human-written software, DNNs can have bugs and can be attacked. To address this, research has explored a wide-range of algorithmic approaches to verify DNN behavior. In this work, we introduce NeuralSAT, a new verification approach that adapts the widely-used DPLL(T) algorithm used in modern SMT solvers. A key feature of SMT solvers is the use of conflict clause learning and search restart to scale verification. Unlike prior DNN verification approaches, NeuralSAT combines an abstraction-based deductive theory solver with clause learning and an evaluation clearly demonstrates the benefits of the approach on a set of challenging verification benchmarks.

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BibTeX RIS

Hai Duong, ThanhVu Nguyen, Matthew Dwyer. 2023-07-17. A DPLL(T) Framework for Verifying Deep Neural Networks. https://arxiv.org/abs/2307.10266

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