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 recently 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.

(2021). ROBY: A Tool for Robustness Analysis of Neural Network Classifiers . Retrieved from http://hdl.handle.net/10446/184998

ROBY: A Tool for Robustness Analysis of Neural Network Classifiers

Arcaini, Paolo;Bombarda, Andrea;Bonfanti, Silvia;Gargantini, Angelo
2021-01-01

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 recently 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.
2021
Arcaini, Paolo; Bombarda, Andrea; Bonfanti, Silvia; Gargantini, Angelo Michele
File allegato/i alla scheda:
File Dimensione del file Formato  
ICST_2021_Tools_cameraReady.pdf

Solo gestori di archivio

Versione: postprint - versione referata/accettata senza referaggio
Licenza: Licenza default Aisberg
Dimensione del file 1.4 MB
Formato Adobe PDF
1.4 MB Adobe PDF   Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/184998
Citazioni
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 6
social impact