A DPLL(T) Framework for Verifying Deep Neural Networks

• URL: http://arxiv.org/abs/2307.10266v3
• Date: Fri, 19 Jan 2024 23:51:05 GMT
• Title: A DPLL(T) Framework for Verifying Deep Neural Networks
• Authors: Hai Duong, ThanhVu Nguyen, Matthew Dwyer
• Abstract summary: Like human-written software, Deep Neural Networks (DNNs) can have bugs and can be attacked. We introduce NeuralSAT, a new verification approach that adapts the widely-used DPLL(T) algorithm used in modern SMT solvers.
• Score: 9.422860826278788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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