  It is common practice to apply padding prior to convolution operations to
preserve the resolution of feature-maps in Convolutional Neural Networks (CNN).
While many alternatives exist, this is often achieved by adding a border of
zeros around the inputs. In this work, we show that adversarial attacks often
result in perturbation anomalies at the image boundaries, which are the areas
where padding is used. Consequently, we aim to provide an analysis of the
interplay between padding and adversarial attacks and seek an answer to the
question of how different padding modes (or their absence) affect adversarial
robustness in various scenarios.
