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While Feedforward Neural Networks (FNNs) have achieved remarkable success invarious tasks, they are vulnerable to adversarial examples. Several techniqueshave been developed to verify the adversarial robustness of FNNs, but most ofthem focus on robustness verification against the local perturbationneighborhood of a single data point. There is still a large research gap inglobal robustness analysis. The global-robustness verifiable frameworkDeepGlobal has been proposed to identify \textit{all} possible AdversarialDangerous Regions (ADRs) of FNNs, not limited to data samples in a test set. Inthis paper, we propose a complete specification and implementation ofDeepGlobal utilizing the SMT solver Z3 for more explicit definition, andpropose several improvements to DeepGlobal for more efficient verification. Toevaluate the effectiveness of our implementation and improvements, we conductextensive experiments on a set of benchmark datasets. Visualization of ourexperiment results shows the validity and effectiveness of the approach.

Using Z3 for Formal Modeling and Verification of FNN Global Robustness