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dc.contributor.authorCatak, Ferhat Özgur
dc.contributor.authorSivaslioglu, Samed
dc.contributor.authorGul, Ensar
dc.date.accessioned2020-04-03T10:23:50Z
dc.date.available2020-04-03T10:23:50Z
dc.date.created2020-01-17T11:22:27Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-1904-5
dc.identifier.urihttps://hdl.handle.net/11250/2650281
dc.description.abstractNowadays, machine learning is being used widely. There have also been attacks towards machine learning process. In this study, robustness against machine learning model attacks which cause many results such as misclassification, disruption of decision mechanisms and avoidance of filters has been shown by autoencoding and with non-targeted attacks to a model trained with Mnist dataset. In this work, the results and improvements for the most common and important attack method, non-targeted attack are presented.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.titleIncrementing Adversarial Robustness with Autoencoding for Machine Learning Model Attacksen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber4en_US
dc.identifier.doi10.1109/SIU.2019.8806432
dc.identifier.cristin1775618
dc.description.localcode© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
cristin.unitcode194,63,30,0
cristin.unitnameInstitutt for informasjonssikkerhet og kommunikasjonsteknologi
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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