Abstract
Classification using Artificial Neural Networks (ANNs) is widely applied in critical domains, such as autonomous driving and
in the medical practice; therefore, their validation is extremely important. A common approach consists in assessing the network
robustness, i.e., its ability to correctly classify input data that is particularly challenging for classification. We re-
cently proposed a robustness definition that considers input data degraded by alterations that may occur in reality; the approach
was originally devised for image classification in the medical domain. In this paper, we extend the definition of robustness
to any type of input for which some alterations can be defined. Then, we present ROBY, a tool for ROBustness analYsis of ANNs.
The tool accepts different types of data (images, sounds, text, etc.) stored either locally or on Google Drive. The user can
use some alterations provided by the tool, or define their own. The robustness computation can be performed either locally
or remotely on Google Colab. The tool has been experimented for robustness computation of image and sound classifiers, used
in the medical and automotive domains.
[download the pdf file] [DOI] [The tool is available at https://github.com/fmselab/roby]