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Published in:
2008 | OriginalPaper | Chapter
Adversarial Pattern Classification Using Multiple Classifiers and Randomisation
Authors : Battista Biggio, Giorgio Fumera, Fabio Roli
Published in: Structural, Syntactic, and Statistical Pattern Recognition
Publisher: Springer Berlin Heidelberg
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In many security applications a pattern recognition system faces an
adversarial classification
problem, in which an intelligent, adaptive adversary modifies patterns to evade the classifier. Several strategies have been recently proposed to make a classifier harder to evade, but they are based only on qualitative and intuitive arguments. In this work, we consider a strategy consisting in hiding information about the classifier to the adversary through the introduction of some randomness in the decision function. We focus on an implementation of this strategy in a multiple classifier system, which is a classification architecture widely used in security applications. We provide a formal support to this strategy, based on an analytical framework for adversarial classification problems recently proposed by other authors, and give an experimental evaluation on a spam filtering task to illustrate our findings.