  Adversarial attacks in the form of imperceptible perturbations of normal
images have been extensively studied, and for every new defense methodology
created, multiple adversarial attacks are found to counteract it. In
particular, a popular style of attack, exemplified in recent years by DeepFool
and Carlini-Wagner, relies solely on white-box scenarios in which full access
to the predictive model and its weights are required. In this work, we instead
propose distinct model-agnostic benchmark perturbations of images in order to
investigate the resilience and robustness of different network architectures.
Results empirically determine that increasing depth within most types of
Convolutional Neural Networks typically improves model resilience towards
general attacks, with improvement steadily decreasing as the model becomes
deeper. Additionally, we find that a notable difference in adversarial
robustness exists between residual architectures with skip connections and
non-residual architectures of similar complexity. Our findings provide
direction for future understanding of residual connections and depth on network
robustness.
