  Although ImageNet was initially proposed as a dataset for performance
benchmarking in the domain of computer vision, it also enabled a variety of
other research efforts. Adversarial machine learning is one such research
effort, employing deceptive inputs to fool models in making wrong predictions.
To evaluate attacks and defenses in the field of adversarial machine learning,
ImageNet remains one of the most frequently used datasets. However, a topic
that is yet to be investigated is the nature of the classes into which
adversarial examples are misclassified. In this paper, we perform a detailed
analysis of these misclassification classes, leveraging the ImageNet class
hierarchy and measuring the relative positions of the aforementioned type of
classes in the unperturbed origins of the adversarial examples. We find that
$71\%$ of the adversarial examples that achieve model-to-model adversarial
transferability are misclassified into one of the top-5 classes predicted for
the underlying source images. We also find that a large subset of untargeted
misclassifications are, in fact, misclassifications into semantically similar
classes. Based on these findings, we discuss the need to take into account the
ImageNet class hierarchy when evaluating untargeted adversarial successes.
Furthermore, we advocate for future research efforts to incorporate categorical
information.
