Metode za obranu od napada na neuronske mreže pomoću augmentacije podataka

master's thesis

• Title (english): Methods for Defense Against Backdoor Attacks Upon Neural Networks Using Data Augmentation
• Author: Antun Herkov
• Mentor: Sven Lončarić (mentor)
• Committee member: Sven Lončarić (predsjednik povjerenstva)
• Committee member: Marko Subašić (član povjerenstva)
• Committee member: Tomislav Petković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2023-07-03, Croatia

Abstract

Dan je pregled sigurnosnih opasnosti koji prijete modelima dubokog učenja uz fokus na
utjecaj primjene augmentacije podataka na sigurnost, metrike i performanse modela
dubokog učenja. Formalno su opisani backdoor napadi na modele te je dan pregled
obrambenih metoda. Za provedbu eksperimenata, razvijena je programska implementacija
procesa učenja inficiranih modela te provođenja obrane u obliku augmentacije podataka
točno definiranim strategijama transformacija slika u Keras i PyTorch frameworcima te je
pokazan utjecaj augmentacije podataka na osiguravanje modela od umetnutoga backdoora.

Abstract (english)

An overview of security threats to deep learning models is given, with a focus on the impact
of applying data augmentation on the security, metrics, and performance of deep learning
models. Backdoor attacks on models are formally described and an overview of defense
methods is given. To carry out the experiments, a software implementation of the process of
training infected models and a defense method in the form of data augmentation with
precisely defined image transformation policies was developed in Keras and PyTorch
frameworks, and the impact of data augmentation on the model security from an inserted
backdoor was demonstrated.

• Keywords: duboko učenje; sigurnost; backdoor napadi; augmentacija podataka
• Keywords (english): deep learning; security; backdoor attacks; data augmentation
• Language: croatian
• URN:NBN: urn:nbn:hr:168:970317
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-10-11 09:01:35