  While Feedforward Neural Networks (FNNs) have achieved remarkable success in
various tasks, they are vulnerable to adversarial examples. Several techniques
have been developed to verify the adversarial robustness of FNNs, but most of
them focus on robustness verification against the local perturbation
neighborhood of a single data point. There is still a large research gap in
global robustness analysis. The global-robustness verifiable framework
DeepGlobal has been proposed to identify \textit{all} possible Adversarial
Dangerous Regions (ADRs) of FNNs, not limited to data samples in a test set. In
this paper, we propose a complete specification and implementation of
DeepGlobal utilizing the SMT solver Z3 for more explicit definition, and
propose several improvements to DeepGlobal for more efficient verification. To
evaluate the effectiveness of our implementation and improvements, we conduct
extensive experiments on a set of benchmark datasets. Visualization of our
experiment results shows the validity and effectiveness of the approach.
