  Deep neural networks (DNNs) have gained prominence in various applications,
such as classification, recognition, and prediction, prompting increased
scrutiny of their properties. A fundamental attribute of traditional DNNs is
their vulnerability to modifications in input data, which has resulted in the
investigation of adversarial attacks. These attacks manipulate the data in
order to mislead a DNN. This study aims to challenge the efficacy and
generalization of contemporary defense mechanisms against adversarial attacks.
Specifically, we explore the hypothesis proposed by Ilyas et. al, which posits
that DNN image features can be either robust or non-robust, with adversarial
attacks targeting the latter. This hypothesis suggests that training a DNN on a
dataset consisting solely of robust features should produce a model resistant
to adversarial attacks. However, our experiments demonstrate that this is not
universally true. To gain further insights into our findings, we analyze the
impact of adversarial attack norms on DNN representations, focusing on samples
subjected to $L_2$ and $L_{\infty}$ norm attacks. Further, we employ canonical
correlation analysis, visualize the representations, and calculate the mean
distance between these representations and various DNN decision boundaries. Our
results reveal a significant difference between $L_2$ and $L_{\infty}$ norms,
which could provide insights into the potential dangers posed by $L_{\infty}$
norm attacks, previously underestimated by the research community.
