  Adversarial examples are inputs to machine learning models that an attacker
has intentionally designed to confuse the model into making a mistake. Such
examples pose a serious threat to the applicability of machine-learning-based
systems, especially in life- and safety-critical domains. To address this
problem, the area of adversarial robustness investigates mechanisms behind
adversarial attacks and defenses against these attacks. This survey reviews a
particular subset of this literature that focuses on investigating properties
of training data in the context of model robustness under evasion attacks. It
first summarizes the main properties of data leading to adversarial
vulnerability. It then discusses guidelines and techniques for improving
adversarial robustness by enhancing the data representation and learning
procedures, as well as techniques for estimating robustness guarantees given
particular data. Finally, it discusses gaps of knowledge and promising future
research directions in this area.
