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.
In support of airborne radar detection missions that rely on Synthetic Aperture Radar (SAR) imagery, there is a need for extensive sets of training data. Due to a paucity of measured data from some targets of interest, there is sometimes a need to train on only simulated SAR data, and yet detect live targets with high confidence during testing. In support of this mission, many researchers have applied a variety of mathematical techniques to simulate data sets. These techniques range from template matching and simpler statistical methods to deep neural networks (DDNs). They demonstrate that with proper pre-processing, some of these methods can achieve target detection with apparently high confidence. However, for all these papers there is no exact measurement of the differences or similarities in the simulated and measured data that would provide a good predictor of the margins between decision boundaries. Thus, this paper has developed a combination of pre-processing methods and standard metrics that enable the assessment of simulated data quality independent of which target recognition algorithm will be utilized. The results show that for some pre-processing methods the differences in simulated data and measured data do not always lend themselves to the desired ability to train on simulated SAR imagery and test on measured SAR imagery.
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