Machine Learning in Adversarial Settings

Patrick McDaniel, Nicolas Papernot, Z. Berkay Celik

Research output: Contribution to journalArticle

56 Scopus citations

Abstract

Recent advances in machine learning have led to innovative applications and services that use computational structures to reason about complex phenomenon. Over the past several years, the security and machine-learning communities have developed novel techniques for constructing adversarial samples-malicious inputs crafted to mislead (and therefore corrupt the integrity of) systems built on computationally learned models. The authors consider the underlying causes of adversarial samples and the future countermeasures that might mitigate them.

Original languageEnglish (US)
Article number7478523
Pages (from-to)68-72
Number of pages5
JournalIEEE Security and Privacy
Volume14
Issue number3
DOIs
StatePublished - May 1 2016

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Law

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