  Adversarial examples, designed to trick Artificial Neural Networks (ANNs)
into producing wrong outputs, highlight vulnerabilities in these models.
Exploring these weaknesses is crucial for developing defenses, and so, we
propose a method to assess the adversarial robustness of image-classifying
ANNs. The t-distributed Stochastic Neighbor Embedding (t-SNE) technique is used
for visual inspection, and a metric, which compares the clean and perturbed
embeddings, helps pinpoint weak spots in the layers. Analyzing two ANNs on
CIFAR-10, one designed by humans and another via NeuroEvolution, we found that
differences between clean and perturbed representations emerge early on, in the
feature extraction layers, affecting subsequent classification. The findings
with our metric are supported by the visual analysis of the t-SNE maps.
