Analysis of methods for detecting attacks on facial biometric authentication in mobile devices

Authors

DOI:

https://doi.org/10.30837/rt.2023.4.215.02

Keywords:

attack, biometric authentication, protection, camera, face, neural network, Android, Deepfake, GAN, PyTorch Mobile, TensorFlow Lite

Abstract

The object of the research is facial biometric authentication in the context of mobile devices (financial and government applications, etc.) of the Android and IOS platforms. The subject of the research is methods of detecting attacks on biometric authentication by face in mobile devices. The purpose of the work is to investigate the methods of creating and recognizing Deepfake videos, to evaluate the possibility of local recognition of fakes within applications in mobile devices of the Android and IOS platforms.

As a result, the vulnerability of biometric authentication to Deepfake technology was confirmed, methods for recognizing fakes were analyzed, and the effectiveness of using a neural network model for local recognition of Deepfake in a mobile device was experimentally confirmed. Conclusions are drawn about possible ways to optimize the size of the model with an emphasis on maintaining accuracy.

References

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Passport-Photo-Biometric-Statistics [Електронний ресурс]. Режим доступу: https://passport-photo.online/blog/biometric-statistics/#gref

Creswell, Antonia; White, Tom; Dumoulin, Vincent; Arulkumaran, Kai; Sengupta, Biswa; Bharath, Anil A. (2018). Generative Adversarial Networks: An Overview // IEEE Signal Processing Magazine, 35(1), 53–65. DOI: 10.1109/MSP.2017.2765202

Hady A. Khalil;Shady A. Maged; (2021). Deepfakes Creation and Detection Using Deep Learning // International Mobile, Intelligent, and Ubiquitous Computing Conference. 2021. DOI: 10.1109/MIUCC52538.2021.9447642

Zainab Zahid, Ammar Haider, Nosheen Sabahat, Asim Tanwir. Vulnerabilities in Biometric Authentication of Smartphones // 2020 IEEE 23rd International Multitopic Conference (INMIC). 2020. DOI: 10.1109/inmic50486.2020.9318094

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Published

2023-12-25

How to Cite

Dolhanenko, O., Sievierinov, O., Viukhin, D., Kotsiuba, V., & Krepko, A. (2023). Analysis of methods for detecting attacks on facial biometric authentication in mobile devices. Radiotekhnika, 4(215), 13–21. https://doi.org/10.30837/rt.2023.4.215.02

Issue

No. 215 (2023): Radiotekhnika

Section

Articles

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