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In this paper, we investigate the use of pretraining with adversarialnetworks, with the objective of discovering the relationship between networkdepth and robustness. For this purpose, we selectively retrain differentportions of VGG and ResNet architectures on CIFAR-10, Imagenette, and ImageNetusing non-adversarial and adversarial data. Experimental results show thatsusceptibility to adversarial samples is associated with low-level featureextraction layers. Therefore, retraining of high-level layers is insufficientfor achieving robustness. Furthermore, adversarial attacks yield outputs fromearly layers that differ statistically from features for non-adversarialsamples and do not permit consistent classification by subsequent layers. Thissupports common hypotheses regarding the association of robustness with thefeature extractor, insufficiency of deeper layers in providing robustness, andlarge differences in adversarial and non-adversarial feature vectors.

Identifying Layers Susceptible to Adversarial Attacks