Machine Learning in Adversarial Settings
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...
Paper Details
Title
Machine Learning in Adversarial Settings
Published Date
May 1, 2016
Journal
Volume
14
Issue
3
Pages
68 - 72
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