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Title:Razpoznava umetno ustvarjenih slik z metodami strojnega učenja
Authors:ID Premzl, Jan (Author)
ID Zorman, Milan (Mentor) More about this mentor... New window
Files:.pdf MAG_Premzl_Jan_2024.pdf (9,68 MB)
MD5: 5E3E46FF2D11CEF9C17F469FBDE8A9B5
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V zaključnem delu smo izdelali modularno aplikacijo, ki je namenjena napovedovanju, ali je slika realna ali umetno ustvarjena. Za ta namen smo naučili enajst različnih modelov, vsakega s petnajstimi različnimi kombinacijami hiperparemetrov. Na podlagi tega smo dobili rezultate, kjer smo izračunali razne statistične mere in korelacije med rezultati. Poleg servisa za klasifikacijo oz. za učenje modelov smo izdelali tudi servis za ustvarjanje učne množice po lastnih željah, spletno aplikacijo, ki omogoča napovedi, in aplikacijski programski vmesnik, ki služi za komunikacijo med servisom za razpoznavo in spletno aplikacijo.
Keywords:Strojno učenje, nevronske mreže, klasifikacija, PyTorch
Place of publishing:Maribor
Publisher:[J. Premzl]
Year of publishing:2024
PID:20.500.12556/DKUM-89313 New window
UDC:004.932:004:8(043.2)
COBISS.SI-ID:217286659 New window
Publication date in DKUM:11.09.2024
Views:353
Downloads:108
Metadata:XML DC-XML DC-RDF
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:03.07.2024

Secondary language

Language:English
Title:Detection of artificially generated images using machine learning methods
Abstract:We have created a modular application, which is used to predict if an image is real or artificially generated. For this we trained eleven different models, each with fifteen different combinations of hyperparameters. On this basis we got results, where we calculated various statistical measures and correlations between results. In addition to the classification service, we developed a service to create a custom image dataset, a web application, which can predict an image and an application programming interface, which serves as a communication interface between the classification service and the web application.
Keywords:Machine learning, neural networks, classification, PyTorch


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