  The training of deep neural network classifiers results in decision
boundaries which geometry is still not well understood. This is in direct
relation with classification problems such as so called adversarial examples.
We introduce zoNNscan, an index that is intended to inform on the boundary
uncertainty (in terms of the presence of other classes) around one given input
datapoint. It is based on confidence entropy, and is implemented through
sampling in the multidimensional ball surrounding that input. We detail the
zoNNscan index, give an algorithm for approximating it, and finally illustrate
its benefits on four applications, including two important problems for the
adoption of deep networks in critical systems: adversarial examples and corner
case inputs. We highlight that zoNNscan exhibits significantly higher values
than for standard inputs in those two problem classes.
