Authors:
Marcello Di Giammarco
1
;
2
;
Antonella Santone
3
;
Mario Cesarelli
4
;
Fabio Martinelli
5
and
Francesco Mercaldo
3
;
1
Affiliations:
1
Institute for Informatics and Telematics (IIT), National Research Council of Italy (CNR), Pisa, Italy
;
2
Department of Information Engineering, University of Pisa, Pisa, Italy
;
3
Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, Campobasso, Italy
;
4
Department of Engineering, University of Sannio, Benevento, Italy
;
5
Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), Rende (CS), Italy
Keyword(s):
Adversarial Machine Learning, Generative Adversarial Networks, Deep Learning, Retinal Imaging, Robustness, Security.
Abstract:
Adversarial machine learning on medical imaging is one of the many applications for which the evaluation of Generative Adversarial Networks in the medical field has demonstrated remarkable interest. This paper proposes a method in which Convolutional neural Networks are trained and tested on the binary classification of real and fake images, generated through generative adversarial networks. In this paper, the considered experiments are on the RGB fundus retina images of the human eye. Results highlight networks with optimal performance, and completely recognize real/fake classification; however, on the other hand, other networks misclassify the images, enhancing security and reliability problems.