The rising adoption of deep neural networks in safety-critical domains has intensified the need for rigorous verification techniques capable of providing formal guarantees on model behavior. Within this context, local robustness is assessed by determining whether small perturbations around a correctly classified input can alter the network's prediction. If the prediction remains stable within the perturbation domain, the network is certified as safe; any input leading to a different classification constitutes an adversarial vulnerability. While most traditional verifiers provide only a binary answer (proving or disproving the existence of such violations), comparatively little attention has been devoted to the exact geometric characterization of the safe and adversarial sub-regions within the input domain.