NeuralSAT: A High-Performance Verification Tool for Deep Neural Networks

Abstract

Abstract Deep Neural Networks (DNNs) are increasingly deployed in critical applications, where ensuring their safety and robustness is paramount. We present $$_\text {CAV25}$$ CAV 25 , a high-performance DNN verification tool that uses the DPLL(T) framework and supports a wide-range of network architectures and activation functions. Since its debut in VNN-COMP’23, in which it achieved the New Participant Award and ranked 4th overall, $$_\text {CAV25}$$ CAV 25 has advanced significantly, achieving second place in VNN-COMP’24. This paper presents and evaluates the latest development of $$_\text {CAV25}$$ CAV 25 , focusing on the versatility, ease of use, and competitive performance of the tool. $$_\text {CAV25}$$ CAV 25 is available at: https://github.com/dynaroars/neuralsat .