  Pattern classification systems are commonly used in adversarial applications,
like biometric authentication, network intrusion detection, and spam filtering,
in which data can be purposely manipulated by humans to undermine their
operation. As this adversarial scenario is not taken into account by classical
design methods, pattern classification systems may exhibit vulnerabilities,
whose exploitation may severely affect their performance, and consequently
limit their practical utility. Extending pattern classification theory and
design methods to adversarial settings is thus a novel and very relevant
research direction, which has not yet been pursued in a systematic way. In this
paper, we address one of the main open issues: evaluating at design phase the
security of pattern classifiers, namely, the performance degradation under
potential attacks they may incur during operation. We propose a framework for
empirical evaluation of classifier security that formalizes and generalizes the
main ideas proposed in the literature, and give examples of its use in three
real applications. Reported results show that security evaluation can provide a
more complete understanding of the classifier's behavior in adversarial
environments, and lead to better design choices.
