Comparative Study of Facial Spoofing Detection using CNN Architecture

Year : 2024 | Volume : 11 | Issue : 03 | Page : 9 17
    By

    Karan Talwalkar,

  • Aditya Ashok,

  • Kshitij Navale,

  • Aditya Kasar,

  1. Student, Department of Computer Science, SVKM’S (Shri Vile Parle Kelavani Mandal’s) Narsee Monjee Institute of Management Studies, Navi Mumbai, Maharashtra, India
  2. Student, Department of Computer Science, SVKM’S (Shri Vile Parle Kelavani Mandal’s) Narsee Monjee Institute of Management Studies, Navi Mumbai, Maharashtra, India
  3. Student, Department of Computer Science, SVKM’S (Shri Vile Parle Kelavani Mandal’s) Narsee Monjee Institute of Management Studies, Navi Mumbai, Maharashtra, India
  4. Assistant Professor, Department of Computer Science, SVKM’S (Shri Vile Parle Kelavani Mandal’s) Narsee Monjee Institute of Management Studies, Navi Mumbai, Maharashtra, India

Abstract

Facial recognition systems face a high risk of security breach due to various facial spoofing attacks. This challenge was addressed by the study of several deep learning models. This study proposes an idea to detect facial spoofing using deep learning architecture to differentiate live faces form various types of spoofed images/videos using different CNN models. In addition, the study seeks to strengthen security measured in facial recognition system demonstrating that by using deep learning models we can effectively tackle spoofing attack and suggest an efficient and practical framework that can be used in real life scenarios. Systems that use facial recognition have become commonplace for authentication and security reasons. However, they create serious security concerns due to their vulnerability to facial spoofing attacks, which involve tricking the system by presenting images, videos, or 3D masks. Convolutional Neural Networks (CNNs) have become effective tools for identifying such spoofing attempts to counter these attacks. To identify facial spoofing, this study compares and assesses the effectiveness of many CNN architectures in terms of accuracy, computing efficiency, and resilience to different spoofing tactics. This study intends to illustrate the advantages and disadvantages of CNN architecture for improving facial recognition security through an analysis of cutting-edge models.

Keywords: CNN, PCA, AlexNet, GoogleNet, ResNet, Liveness Detection

[This article belongs to Recent Trends in Electronics Communication Systems ]

How to cite this article:
Karan Talwalkar, Aditya Ashok, Kshitij Navale, Aditya Kasar. Comparative Study of Facial Spoofing Detection using CNN Architecture. Recent Trends in Electronics Communication Systems. 2024; 11(03):9-17.
How to cite this URL:
Karan Talwalkar, Aditya Ashok, Kshitij Navale, Aditya Kasar. Comparative Study of Facial Spoofing Detection using CNN Architecture. Recent Trends in Electronics Communication Systems. 2024; 11(03):9-17. Available from: https://journals.stmjournals.com/rtecs/article=2024/view=176882


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Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 07/08/2024
Accepted 12/08/2024
Published 08/09/2024
368


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