Security Vulnerability in Face Mask Monitoring System

The COVID-19 pandemic has increased demand for face mask detection systems that utilize deep learning and machine learning algorithms. However, these systems are susceptible to adversarial attacks, where an attacker can manipulate the system to make incorrect predictions. This study aimed to test th...

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Bibliographic Details
Published in2023 10th International Conference on Computing for Sustainable Global Development (INDIACom) pp. 231 - 237
Main Authors Ul Haque, Sheikh Burhan, Zafar, Aasim, Roshan, Khushnaseeb
Format Conference Proceeding
LanguageEnglish
Published Bharati Vidyapeeth, New Delhi 15.03.2023
Subjects
Adversarial attacks
Adversarial example
COVID-19
Data models
Deep learning
Face Mask recognition
Face recognition
Perturbation methods
Streaming media
Training data
Transfer learning
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Summary:The COVID-19 pandemic has increased demand for face mask detection systems that utilize deep learning and machine learning algorithms. However, these systems are susceptible to adversarial attacks, where an attacker can manipulate the system to make incorrect predictions. This study aimed to test the vulnerability of a deep learning-based face mask detection model to a specific type of attack called a black box adversarial attack in which the attacker possesses only partial information about the target model. The study's findings showed that the attack successfully reduced the model's accuracy from 96.48% to 49.25%. This emphasizes the need for more robust defense mechanisms in face mask detection systems to ensure their reliability.