Making machine learning robust against adversarial inputs: Such inputs distort how machine-learningbased systems are able to function in the world as it is

Ian Goodfellow; Patrick McDaniel; Nicolas Papernot

doi:10.1145/3134599

Making machine learning robust against adversarial inputs: Such inputs distort how machine-learningbased systems are able to function in the world as it is

Ian Goodfellow, Patrick McDaniel, Nicolas Papernot

Research output: Contribution to journal › Article › peer-review

Original language	English (US)
Pages (from-to)	56-66
Number of pages	11
Journal	Communications of the ACM
Volume	61
Issue number	7
DOIs	https://doi.org/10.1145/3134599
State	Published - Jul 2018

All Science Journal Classification (ASJC) codes

General Computer Science

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10.1145/3134599

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title = "Making machine learning robust against adversarial inputs: Such inputs distort how machine-learningbased systems are able to function in the world as it is",

author = "Ian Goodfellow and Patrick McDaniel and Nicolas Papernot",

note = "Funding Information: Author Nicolas Papernot is supported by a Google Ph.D. Fellowship in Secu rity. Research was supported in part by the Army Research Laboratory under Cooperative Agreement Number W911NF-13-2-0045 (ARL Cyber Security CRA) and the Army Research Office under grant W911NF-13-1-0421. The views and conclusions contained in this article are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. government. The U.S. government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation hereon.",

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AU - McDaniel, Patrick

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N1 - Funding Information: Author Nicolas Papernot is supported by a Google Ph.D. Fellowship in Secu rity. Research was supported in part by the Army Research Laboratory under Cooperative Agreement Number W911NF-13-2-0045 (ARL Cyber Security CRA) and the Army Research Office under grant W911NF-13-1-0421. The views and conclusions contained in this article are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. government. The U.S. government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation hereon.

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Making machine learning robust against adversarial inputs: Such inputs distort how machine-learningbased systems are able to function in the world as it is

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