Adversarna motnja razpoznave slik nevronske mreže s pomočjo evolucijskega algoritma : diplomsko delo

Kukovec, Rok

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Title:	Adversarna motnja razpoznave slik nevronske mreže s pomočjo evolucijskega algoritma : diplomsko delo
Authors:	ID Kukovec, Rok (Author) ID Karakatič, Sašo (Mentor) More about this mentor... ID Fister, Iztok (Comentor)
Files:	UN_Kukovec_Rok_2021.pdf (5,61 MB) MD5: E0869F13ECED2B2D5059AB2445338DFE PID: 20.500.12556/dkum/56ed871e-cbeb-4e16-a0ba-6a15cb97cb39
Language:	Slovenian
Work type:	Bachelor thesis/paper
Typology:	2.11 - Undergraduate Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	Uspešnost prepoznavanja slik z uporabo nevronskih mrež je odvisna od parametrov in filtrov, optimiziranih skozi učni proces. Tukaj najdemo razliko v načinu prepoznavanja motivov med ljudmi in stroji. Pojavi se vrzel, ki jo napadalec s pomočjo adversarnih motenj lahko izkoristi. Slike so na videz neopazno spremenjene, ljudje razlike težko zaznajo, vendar klasifikacija nevronske mreže odpove. To delo raziskuje poustvarjanje slik z evolucijskim algoritmom. Konvolucijska nevronska mreža AlexNet po spremembi ne more prepoznati predhodno jasnih motivov. Človeku prepoznavna slika se ohrani. Pari izvirnih in poustvarjenih slik so bili primerjani z uporabo vizualne ocene in statističnih metrik.
Keywords:	adversarna motnja, evolucijski algoritmi, konvolucijske nevronske mreže, računalniški vid
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[R. Kukovec]
Year of publishing:	2021
Number of pages:	XIII, 68 str.
PID:	20.500.12556/DKUM-79575
UDC:	004.932:004.8.021(043.2)
COBISS.SI-ID:	79865859
Publication date in DKUM:	24.08.2021
Views:	1798
Downloads:	187
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	23.07.2021

Secondary language

Language:	English
Title:	Adversarial perturbation on neural network image recognition using an evolutionary algorithm
Abstract:	Neural networks used for image recognition heavily depend on filters and parameters optimized throughout the learning process. The difference between the way people and machines see and recognize everyday objects emerge and an attacker can use it to their advantage. The images are seemingly imperceptibly altered so that people have difficulties detecting the changes, but the classification of the neural network fails. This work explores recreating images using an evolutionary algorithm. Convolutional neural network Alexnet no longer recognizes previously clear motifs. The human recognizable image is preserved. Pairs of original and recreated images were compared using visual estimation and statistical metrics.
Keywords:	Adversarial perturbation, Convolutional neural network, Avolutional algorithms, Machine vision

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