Abstract
Today biometric identity authentication technologies are widespread. These systems are implemented not only in enterprises, controlled-access facilities, but also on smartphones of ordinary users and in online applications. The problem of choosing one of the authentication methods remains urgent. This paper provides a comparative analysis of existing systems and concludes that one of the most common and persistent methods is facial authentication system. The most powerful types of attacks on the biometric system are attacks on the database of biometric templates and attacks on sensors for obtaining biometric characteristics. Attacks on biometric sensors or spoofing attack is aimed at impersonating another person through fake biometric data. The paper deals with the possibility of special attacks on the biometric system of authentication by face image. A new method of detecting fake attacks (spoofing attacks) is proposed. The method is based on the use of an artificial convolutional neural network which was trained using a Replay-Attack Database from Idiap Research Institute. The obtained results show high efficiency of the proposed method of detecting spoofing attacks: the probability that an attack will be detected is 94.98%.
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References
Tsarov, R.Yu., Lemeha, T.M.: Biometric technology, Odessa (2016). 140 p.
Face recognition technology - a new era in video analytics, video surveillance and access control systems. https://securityrussia.com/blog/face-recognition.html
Lutsenko, M., Kuznetsov, A., Gorbenko, Y.: Key generation from biometric data of Iris. In: The Fourth International Conference on Information and Telecommunication Technologies and Radio Electronics, UkrMiCo 2019, Odessa (2019)
Lutsenko, M., Kuznetsov, A., Gorbenko, Y., Oleshko, I., Pronchakov, Y., Kotukh, Y.: Key generation from biometric data of Iris. In: 2019 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo), Odessa (2019 in press)
Lavrentieva, G.M., Novoselova, S.A., Kozlova, A.V., et al.: Detection methods for spoofing attacks of repeated reproduction on voice biometric systems. Sci. Tech. J. Inf. Technol. Mech. Opt. 18(3), 428–436 (2018)
Yang, J.: Learn convolutional neural network for face anti-spoofing. arXiv preprint arXiv:1408.5601 (2014)
Xu, Z.: Learning temporal features using the LSTM-CNN architecture for face anti-spoofing. In: ACPR, pp. 41–45 (2015)
FaceNet: A Unified Embedding for Face Recognition and Clustering. https://arxiv.org/pdf/1503.03832.pdf
Lazarick, R.: Spoofs, subversion and suspicion: terms and concepts. In: Proceedings of the NIST International Biometric Performance Conference (IBPC) (2012)
Matsugu, M., Mori, K., Mitari, Y., Kaneda, Y.: Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw. 16(5–6), 555–559 (2003)
Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. University of Washington. https://arxiv.org/abs/1804.02767
YOLO: Real-Time Object Detection. https://pjreddie.com/darknet/yolo/
Darknet. https://github.com/pjreddie/darknet
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by Error Propagation, 1 September 1985. https://doi.org/10.21236/ada164453
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539
Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: BIOSIG-2012. IEEE BIOSIG (2012)
Ting, K.M.: Precision and recall. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning. Springer, Boston (2011)
Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. Submitted on 22 Dec 2014 (v1), last revised 30 Jan 2017 (this version, v9). https://arxiv.org/abs/1412.6980
Janocha, K., Czarnecki, W.M.: On Loss Functions for Deep Neural Networks in Classification. Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland (2017)
Abadi, M., Agarwal, A., Barham, P., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.org
Bradski, G.: The OpenCV library, Dr. Dobb’s J. Softw. Tools 25, 120–125 (2000)
Jung, A.B.: imgaug (2018). https://github.com/aleju/imgaug
Kuznetsov, A., Kiyan, A., Uvarova, A., Serhiienko, R., Smirnov, V.: New code based fuzzy extractor for biometric cryptography. In: 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), Kharkiv, Ukraine, pp. 119–124 (2018). https://doi.org/10.1109/INFOCOMMST.2018.8632040
Rassomakhin, S., Kuznetsov, A., Shlokin, V., Belozertsev, I., Serhiienko, R.: Mathematical model for the probabilistic minutia distribution in biometric fingerprint images. In: 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, pp. 514–518 (2018). https://doi.org/10.1109/dsmp.2018.8478496
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Kuznetsov, A., Oleshko, I., Chernov, K., Bagmut, M., Smirnova, T. (2021). Biometric Authentication Using Convolutional Neural Networks. In: Ilchenko, M., Uryvsky, L., Globa, L. (eds) Advances in Information and Communication Technology and Systems. MCT 2019. Lecture Notes in Networks and Systems, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-030-58359-0_6
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