Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification
Paper summary Cao and Gong introduce region-based classification as defense against adversarial examples. In particular, given an input (benign test input or adversarial example), the method samples random point in the neighborhood and classifies the test sample according to the majority vote of the obtained labels. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
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Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification
Xiaoyu Cao and Neil Zhenqiang Gong
Proceedings of the 33rd Annual Computer Security Applications Conference on - ACSAC 2017 - 2017 via Local CrossRef
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Summary by David Stutz 3 months ago
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