Face recognition system against adversarial attack using convolutional neural network
Joint Authors
Source
The Iraqi Journal of Electrical and Electronic Engineering
Issue
Vol. 18, Issue 1 (30 Jun. 2022), pp.1-8, 8 p.
Publisher
University of Basrah College of Engineering
Publication Date
2022-06-30
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Face recognition is the technology that verifies or recognizes faces from images, videos, or real-time streams.
it can be used in security or employee attendance systems.
face recognition systems may encounter some attacks that reduce their ability to recognize faces properly.
so, many noisy images mixed with original ones lead to confusion in the results.
various attacks that exploit this weakness affect the face recognition systems such as fast gradient sign method (FGSM), deep fool, and projected gradient descent (PGD).
this paper proposes a method to protect the face recognition system against these attacks by distorting images through different attacks, then training the recognition deep network model, specifically convolutional neural network (CNN), using the original and distorted images.
diverse experiments have been conducted using combinations of original and distorted images to test the effectiveness of the system.
the system showed an accuracy of 93% using FGSM attack, 97% using deep fool, and 95% using PGD.
American Psychological Association (APA)
Kazim, Ansam& al-Darraji, Salah. 2022. Face recognition system against adversarial attack using convolutional neural network. The Iraqi Journal of Electrical and Electronic Engineering،Vol. 18, no. 1, pp.1-8.
https://search.emarefa.net/detail/BIM-1380202
Modern Language Association (MLA)
Kazim, Ansam& al-Darraji, Salah. Face recognition system against adversarial attack using convolutional neural network. The Iraqi Journal of Electrical and Electronic Engineering Vol. 18, no. 1 (Jun. 2022), pp.1-8.
https://search.emarefa.net/detail/BIM-1380202
American Medical Association (AMA)
Kazim, Ansam& al-Darraji, Salah. Face recognition system against adversarial attack using convolutional neural network. The Iraqi Journal of Electrical and Electronic Engineering. 2022. Vol. 18, no. 1, pp.1-8.
https://search.emarefa.net/detail/BIM-1380202
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 8
Record ID
BIM-1380202