What's Data Got To Do With It

Continued advancements in adversarial attacks have crippled neural network performance. These small pixel perturbations can go undetected and cause networks to misclassify with high confidence. The motivation for this paper was to investigate how various sensor modalities and network models respond to adversarial attacks. It is important to realize that the large diversity in neural network architectures makes it difficult for any analytical conclusions to be made that generalize across any given neural network. For this reason, we share the statistical analyses performed which could be applied to any network under review. General observations gained from this analysis are also shared which indicated that network classification accuracy is not just a function of the network model but the data as well.