Learning Securely.
The article discusses machine learning and an emerging field focused on exploring vulnerabilities in machine learning algorithms and ways to handle adversarial input by hackers. Topics include the impact of deep neural networks on potential applications and attacks of machine learning, the ability o...
| Published in: | Communications of the ACM Vol. 59; no. 11; pp. 12 - 15 |
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| Format: | Article |
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Association for Computing Machinery
Nov2016
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=119379428&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 119379428 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Nov2016 vid: 59 iid: 11 pid: 68 pub: Association for Computing Machinery artinfo: ui: 119379428 10.1145/2994577 ppf: 12 ppct: 3 formats: tig: atl: Learning Securely. aug: au: Klarreich, Erica affil: Mathematics and science journalist based in Berkeley, CA su: Machine learning Prevention of computer hacking Artificial neural networks Computer security Deep learning sug: subj: Machine learning Prevention of computer hacking Artificial neural networks Computer security Deep learning ab: The article discusses machine learning and an emerging field focused on exploring vulnerabilities in machine learning algorithms and ways to handle adversarial input by hackers. Topics include the impact of deep neural networks on potential applications and attacks of machine learning, the ability of hackers to transfer adversarial examples from one machine learning model to another, and the need for benchmarks and the integration of security during the development of machine learning algorithms. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2016 holdings: @attributes: islocal: N |
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