1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchChris Meekhttps://www.microsoft.com/en-us/research/people/meek/Adversarial Learning - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="HWwW6Zvzj5"><a href="https://www.microsoft.com/en-us/research/publication/adversarial-learning/">Adversarial Learning</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/adversarial-learning/embed/#?secret=HWwW6Zvzj5" width="600" height="338" title="“Adversarial Learning” — Microsoft Research" data-secret="HWwW6Zvzj5" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier. In this paper, we introduce the adversarial classifier reverse engineering (ACRE) learning problem, the […]