Is Face Recognition Safe from Realizable Attacks?
| Autor: | Sanjay Saha, Terence Sim |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Scheme (programming language) Computer Science - Machine Learning Biometrics Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Computer security computer.software_genre Facial recognition system Machine Learning (cs.LG) Identification (information) 020204 information systems Face (geometry) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing computer Vulnerability (computing) computer.programming_language |
| Zdroj: | IJCB |
| DOI: | 10.1109/ijcb48548.2020.9304864 |
| Popis: | Face recognition is a popular form of biometric authentication and due to its widespread use, attacks have become more common as well. Recent studies show that Face Recognition Systems are vulnerable to attacks and can lead to erroneous identification of faces. Interestingly, most of these attacks are white-box, or they are manipulating facial images in ways that are not physically realizable. In this paper, we propose an attack scheme where the attacker can generate realistic synthesized face images with subtle perturbations and physically realize that onto his face to attack black-box face recognition systems. Comprehensive experiments and analyses show that subtle perturbations realized on attackers face can create successful attacks on state-of-the-art face recognition systems in black-box settings. Our study exposes the underlying vulnerability posed by the Face Recognition Systems against realizable black-box attacks. Comment: 2020 IEEE International Joint Conference on Biometrics (IJCB) |
| Databáze: | OpenAIRE |
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