Deep Neural Networks (DNNs) have emerged as an effective approach to tackling
real-world problems. However, like human-written software,
automatically-generated DNNs can have bugs and be attacked. This thus attracts
many recent interests in developing effective and scalable DNN verification
techniques and tools. In this work, we introduce a NeuralSAT, a new constraint
solving approach to DNN verification. The design of NeuralSAT follows the
DPLL(T) algorithm used modern SMT solving, which includes (conflict) clause
learning, abstraction, and theory solving, and thus NeuralSAT can be considered
as an SMT framework for DNNs. Preliminary results show that the NeuralSAT
prototype is competitive to the state-of-the-art. We hope, with proper
optimization and engineering, NeuralSAT will carry the power and success of
modern SAT/SMT solvers to DNN verification. NeuralSAT is avaliable from:
https://github.com/dynaroars/neuralsat-solverComment: 27 pages, 8 figures. NeuralSAT is avaliable from:
https://github.com/dynaroars/neuralsat-solve