Classifier Evasion: Models and Open Problems

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Provided by: Springer Healthcare
Topic: Software
Format: PDF
As a growing number of software developers apply machine learning to make key decisions in their systems, adversaries are adapting and launching ever more sophisticated attacks against these systems. The near-optimal evasion problem considers an adversary that searches for a low-cost negative instance by submitting a minimal number of queries to a classifier, in order to effectively evade the classifier. In this paper, the authors posit several open problems and alternative variants to the near-optimal evasion problem. Solutions to these problems would significantly advance the state-of-the-art in secure machine learning.
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