Please use this identifier to cite or link to this item:
Title: Evaluation of adversarial attacks against deep learning models
Authors: Ta, Anh Duc
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Ta, A. D. (2022). Evaluation of adversarial attacks against deep learning models. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE21-0250
Abstract: The rapid development of deep learning techniques has made them useful in many applications. However, recent studies have shown that deep learning algorithms can be vulnerable to adversarial attacks. This is a serious concern when considering these algorithms for safety-critical applications. To further improve the defense of deep learning algorithm, there is a need to study the threats of adversarial attacks. In this project, the effectiveness of adversarial attacks on deep learning models was evaluated under different criteria like different attack methods, different deep learning model structures and different deep learning tasks. The result of the experiment showed that the effectiveness of the attacks depended on the type of the attack, the source model structure, and the target model structure. Moreover, the result indicated that adversarial training is not the best defense technique against all types of attack methods. Furthermore, the report also showed that effectiveness of adversarial examples is not limited to Computer Vision tasks only but also to Audio Examples Classification.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
729.03 kBAdobe PDFView/Open

Page view(s)

Updated on Dec 10, 2023

Download(s) 50

Updated on Dec 10, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.