Open Access
ARTICLE
VeriFace: Defending against Adversarial Attacks in Face Verification Systems
Awny Sayed1, Sohair Kinlany2, Alaa Zaki2, Ahmed Mahfouz2,3,*
1
Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah,
Saudi Arabia
2
Computer Science Department, Faculty of Science, Minia University, Al Minya, Egypt
3
Faculty of Computer Studies, Arab Open University, Muscat, Oman
* Corresponding Author: Ahmed Mahfouz. Email:
Computers, Materials & Continua 2023, 76(3), 3151-3166. https://doi.org/10.32604/cmc.2023.040256
Received 11 March 2023; Accepted 13 June 2023; Issue published 08 October 2023
Abstract
Face verification systems are critical in a wide range of applications, such as security systems and biometric
authentication. However, these systems are vulnerable to adversarial attacks, which can significantly compromise
their accuracy and reliability. Adversarial attacks are designed to deceive the face verification system by adding
subtle perturbations to the input images. These perturbations can be imperceptible to the human eye but can cause
the system to misclassify or fail to recognize the person in the image. To address this issue, we propose a novel system
called VeriFace that comprises two defense mechanisms, adversarial detection, and adversarial removal. The first
mechanism, adversarial detection, is designed to identify whether an input image has been subjected to adversarial
perturbations. The second mechanism, adversarial removal, is designed to remove these perturbations from the
input image to ensure the face verification system can accurately recognize the person in the image. To evaluate the
effectiveness of the VeriFace system, we conducted experiments on different types of adversarial attacks using the
Labelled Faces in the Wild (LFW) dataset. Our results show that the VeriFace adversarial detector can accurately
identify adversarial images with a high detection accuracy of 100%. Additionally, our proposed VeriFace adversarial
removal method has a significantly lower attack success rate of 6.5% compared to state-of-the-art removal methods.
Keywords
Cite This Article
APA Style
Sayed, A., Kinlany, S., Zaki, A., Mahfouz, A. (2023). Veriface: defending against adversarial attacks in face verification systems. Computers, Materials & Continua, 76(3), 3151-3166. https://doi.org/10.32604/cmc.2023.040256
Vancouver Style
Sayed A, Kinlany S, Zaki A, Mahfouz A. Veriface: defending against adversarial attacks in face verification systems. Comput Mater Contin. 2023;76(3):3151-3166 https://doi.org/10.32604/cmc.2023.040256
IEEE Style
A. Sayed, S. Kinlany, A. Zaki, and A. Mahfouz "VeriFace: Defending against Adversarial Attacks in Face Verification Systems," Comput. Mater. Contin., vol. 76, no. 3, pp. 3151-3166. 2023. https://doi.org/10.32604/cmc.2023.040256