Abstract
SW-based systems depend more and more on AI also for critical tasks. For instance, the use of machine learning, especially
for image recognition, is increasing ever more. As state-of-the-art, Convolutional Neural Networks (CNNs) are the most adopted
techniques for image classification. Although they are proved to have optimal results, it is not clear what happens when unforeseen
modifications during the image acquisition and elaboration occur. Thus, it is very important to assess the robustness of a
CNN, especially when it is used in a safety critical system, as, e.g., in the medical domain or in automated driving systems.
Most of the analyses made about the robustness of CNNs are focused on adversarial examples which are created by exploiting
the CNN internal structure; however, these are not the only problems we can encounter with CNNs and, moreover, they may be
unlikely in some fields. This is why, in this paper, we focus on the robustness analysis when plausible alterations caused
by an error during the acquisition of the input images occur. We give a novel definition of robustness w.r.t. possible input
alterations for a CNN and we propose a framework to compute it. Moreover, we analyse four methods (data augmentation, limited
data augmentation, network parallelization, and limited network parallelization) which can be used to improve the robustness
of a CNN for image classification. Analyses are conducted over a dataset of histologic images.
[download the pdf file] [DOI] [This paper won the **best paper award**]