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Adversarial examples are inputs to machine learning models that an attackerhas intentionally designed to confuse the model into making a mistake. Suchexamples pose a serious threat to the applicability of machine-learning-basedsystems, especially in life- and safety-critical domains. To address thisproblem, the area of adversarial robustness investigates mechanisms behindadversarial attacks and defenses against these attacks. This survey reviews aparticular subset of this literature that focuses on investigating propertiesof training data in the context of model robustness under evasion attacks. Itfirst summarizes the main properties of data leading to adversarialvulnerability. It then discusses guidelines and techniques for improvingadversarial robustness by enhancing the data representation and learningprocedures, as well as techniques for estimating robustness guarantees givenparticular data. Finally, it discusses gaps of knowledge and promising futureresearch directions in this area.

It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness