How Deep Learning Sees the World: A Survey on Adversarial Attacks &
  Defenses
Joana C. Costa Tiago Roxo Hugo Proença Pedro R. M. Inácio
Deep Learning is currently used to perform multiple tasks, such as object
recognition, face recognition, and natural language processing. However, Deep
Neural Networks (DNNs) are vulnerable to perturbations that alter the network
prediction (adversarial examples), raising concerns regarding its usage in
critical areas, such as self-driving vehicles, malware detection, and
healthcare. This paper compiles the most recent adversarial attacks, grouped by
the attacker capacity, and modern defenses clustered by protection strategies.
We also present the new advances regarding Vision Transformers, summarize the
datasets and metrics used in the context of adversarial settings, and compare
the state-of-the-art results under different attacks, finishing with the
identification of open issues.