DeepCDCL: An CDCL-based Neural Network Verification Framework
Zongxin Liu Pengfei Yang Lijun Zhang Xiaowei Huang
Neural networks in safety-critical applications face increasing safety and
security concerns due to their susceptibility to little disturbance. In this
paper, we propose DeepCDCL, a novel neural network verification framework based
on the Conflict-Driven Clause Learning (CDCL) algorithm. We introduce an
asynchronous clause learning and management structure, reducing redundant time
consumption compared to the direct application of the CDCL framework.
Furthermore, we also provide a detailed evaluation of the performance of our
approach on the ACAS Xu and MNIST datasets, showing that a significant speed-up
is achieved in most cases.