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master's thesis
Obrane klasifikacijskih modela od zatrovanih skupova podataka
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:034860
Reiter, Tin
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Reiter, T. (2023). Obrane klasifikacijskih modela od zatrovanih skupova podataka (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:034860

Reiter, Tin. "Obrane klasifikacijskih modela od zatrovanih skupova podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:034860

Reiter, Tin. "Obrane klasifikacijskih modela od zatrovanih skupova podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:034860

Reiter, T. (2023). 'Obrane klasifikacijskih modela od zatrovanih skupova podataka', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 13 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:034860

Reiter T. Obrane klasifikacijskih modela od zatrovanih skupova podataka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 July 13] Available at: https://urn.nsk.hr/urn:nbn:hr:168:034860

T. Reiter, "Obrane klasifikacijskih modela od zatrovanih skupova podataka", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:034860

Metadata
TitleObrane klasifikacijskih modela od zatrovanih skupova podataka
Title (english)Defending classification models from poisoned datasets
AuthorTin Reiter
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
Committee memberZoran Kalafatić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-11, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cilj ovog rada je pokazati načine napada na duboke modele pomoću zatrovanih skupova podataka te istražiti kako ih obraniti od takvih napada. Želimo vidjeti koliko su efektivne trenutne metode obrane dubokih modela od takvih napada. U radu prvo opisujemo strukturu umjetnih neurona i dubokih modela. Zatim objašnjavamo različite načine napada pomoću zatrovanih skupova podataka na duboke modele. Nakon toga objašnjavamo razne načine obrana od tih napada. Na kraju prikazujemo eksperimentalne rezultate napada na duboke modele te efektivnost načina obrane protiv njih.
Abstract (english)
The goal of this paper is to demonstrate backdoor attacks on deep models and ways to mitigate them. We want to see how effective are the current defenses against backdoor attacks. In this paper we first describe the structure of artificial neurons and deep models. Then we describe the different types of defenses that can be used to mitigate backdoor attacks. In the end we show the experimental results gained from backdoor attacks on deep models and the effectiveness of the different methods to mitigate them.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:034860
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
Terms of useLicence In copyright icon
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Created on2023-10-11 10:19:39