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Convolutional Neural Networks to Protect Against Spoofing Attacks on Biometric Face Authentication

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Book cover Digital Transformation (PLAIS EuroSymposium 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 429))

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Abstract

Modern technologies of authentication and authorization of access play a significant role in ensuring the protection of information in various practical applications. We consider the most convenient and used in modern mobile gadgets face authentication, ie when the primary information to provide access are certain features of biometric images of the user’s face. Most of the systems use intelligent processing of biometric images, in particular, artificial intelligence technology and deep learning. But at the same time, as always in cybersecurity, technologies for violating biometric authentication are being studied and researched. In particular, to date, the most common attack is substitution (spoofing), ie when attackers use pre-recorded biometric images to gain unauthorized access to critical information. For example, this could be a photo and/or video image of a person used to unlock their smartphone. Protection against such attacks is very difficult, because it involves the development and study of technologies for detecting signs of life. The most promising in this direction are artificial intelligence techniques, in particular, convolutional neural networks (CNN). This is the practical application of intelligent processing of biometric images and is studied in this article. We review various CNN settings and configurations and experimentally investigate their effect on the effectiveness of signs of life detection. For this purpose, success and failure indicators of the first and second kind are used, which are estimated by the values of cross entropy. These are reliable and reproducible indicators that characterize the effectiveness of protection against spoofing attacks on biometric authentication on the face. The world-famous TensorFlow and OpenCV libraries are used for field experiments, photos and videos of various users are used as source data, including Replay-Attack Database from Idiap Research Institute.

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References

  1. Rathgeb, C., Uhl, A.: A survey on biometric cryptosystems and cancelable biometrics. EURASIP J. Inf. Secur. 2011, 3 (2011). https://doi.org/10.1186/1687-417X-2011-3

    Article  Google Scholar 

  2. Kakadiaris, I.A., Woodard, D.L., Schuckers, S.A.C.: Selected best works from biometrics: theory, applications, and systems 2019. IEEE Trans. Biom. Behav. Identity Sci. 2, 308–309 (2020). https://doi.org/10.1109/TBIOM.2020.3022299

    Article  Google Scholar 

  3. Lutsenko, M., Kuznetsov, A., Kiian, A., Smirnov, O., Kuznetsova, T.: Biometric cryptosystems: overview, state-of-the-art and perspective directions. In: Ilchenko, M., Uryvsky, L., Globa, L. (eds.) MCT 2019. LNNS, vol. 152, pp. 66–84. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-58359-0_5

    Chapter  Google Scholar 

  4. Mandal, S., Bera, B., Sutrala, A.K., Das, A.K., Choo, K.-K.R., Park, Y.: Certificateless-signcryption-based three-factor user access control scheme for IoT environment. IEEE Internet Things J. 7, 3184–3197 (2020). https://doi.org/10.1109/JIOT.2020.2966242

    Article  Google Scholar 

  5. Banerjee, S., Odelu, V., Das, A.K., Srinivas, J., Kumar, N., Chattopadhyay, S., et al.: A provably secure and lightweight anonymous user authenticated session key exchange scheme for Internet of Things deployment. IEEE Internet Things J. 6, 8739–8752 (2019). https://doi.org/10.1109/JIOT.2019.2923373

    Article  Google Scholar 

  6. Hernández Álvarez, F., Hernández Encinas, L.: Security efficiency analysis of a biometric fuzzy extractor for iris templates. In: Herrero, Á., Gastaldo, P., Zunino, R., Corchado, E. (eds.) Computational Intelligence in Security for Information Systems, pp. 163–170. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04091-7_20

    Chapter  MATH  Google Scholar 

  7. Shreyas, K.K.M., Rajeev, S., Panetta, K., Agaian, S.S.: Fingerprint authentication using geometric features. In: 2017 IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1–7 (2017). https://doi.org/10.1109/THS.2017.7943449

  8. Perazzone, J.B., Yu, P.L., Sadler, B.M., Blum, R.S.: Fingerprint embedding authentication with artificial noise: MISO regime. In: 2019 IEEE Conference on Communications and Network Security (CNS), pp. 1–5 (2019). https://doi.org/10.1109/CNS.2019.8802636

  9. Engelsma, J.J., Cao, K., Jain, A.K.: Fingerprint match in box. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–10 (2018). https://doi.org/10.1109/BTAS.2018.8698556

  10. Chugh, T., Cao, K., Jain, A.K.: Fingerprint spoof buster: use of minutiae-centered patches. IEEE Trans. Inf. Forensics Secur. 13, 2190–2202 (2018). https://doi.org/10.1109/TIFS.2018.2812193

    Article  Google Scholar 

  11. Chuang, C.-W., Fan, C.-P.: Biometric authentication with combined iris and sclera information by YOLO-based deep-learning network. In: 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), pp. 1–2 (2020). https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258253

  12. Hsiao, C.-S., Fan, C.-P., Hwang, Y.-T.: Iris location and recognition by deep-learning networks based design for biometric authorization. In: 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), pp. 144–145 (2021). https://doi.org/10.1109/LifeTech52111.2021.9391787

  13. Boutros, F., Damer, N., Raja, K., Ramachandra, R., Kirchbuchner, F., Kuijper, A.: On benchmarking iris recognition within a head-mounted display for AR/VR applications. In: 2020 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–10 (2020). https://doi.org/10.1109/IJCB48548.2020.9304919

  14. Oldal, L.G., Kovács, A.: Hand geometry and palmprint-based authentication using image processing. In: 2020 IEEE 18th International Symposium on Intelligent Systems and Informatics (SISY), pp. 125–130 (2020). https://doi.org/10.1109/SISY50555.2020.9217068

  15. Gupta, P., Gupta, P.: Multibiometric authentication system using slap fingerprints, palm dorsal vein, and hand geometry. IEEE Trans. Ind. Electron. 65, 9777–9784 (2018). https://doi.org/10.1109/TIE.2018.2823686

    Article  Google Scholar 

  16. Kinkiri, S., Keates, S.: Speaker identification: variations of a human voice. In: 2020 International Conference on Advances in Computing and Communication Engineering (ICACCE), pp. 1–4 (2020). https://doi.org/10.1109/ICACCE49060.2020.9154998

  17. Miguel-Hurtado, O., Blanco-Gonzalo, R., Guest, R., Lunerti, C.: Interaction evaluation of a mobile voice authentication system. In: 2016 IEEE International Carnahan Conference on Security Technology (ICCST), pp. 1–8 (2016). https://doi.org/10.1109/CCST.2016.7815697

  18. Miguel-Hurtado, O., Guest, R., Lunerti, C.: Voice and face interaction evaluation of a mobile authentication platform. In: 2017 International Carnahan Conference on Security Technology (ICCST), pp. 1–6 (2017). https://doi.org/10.1109/CCST.2017.8167860

  19. Face-based multiple user active authentication on mobile devices (n.d.). https://ieeexplore.ieee.org/document/8501956/. Accessed 8 May 2021

  20. A face-recognition approach using deep reinforcement learning approach for user authentication (n.d.). https://ieeexplore.ieee.org/document/8119148/. Accessed 8 May 2021

  21. Beukes, E., Coetzer, J.: Hand vein-based biometric authentication using two-channel similarity measure networks. In: 2020 International SAUPEC/RobMech/PRASA Conference, pp. 1–6 (2020). https://doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041064

  22. Kohlakala, A., Coetzer, J.: On automated ear-based authentication. In: 2020 International SAUPEC/RobMech/PRASA Conference, pp. 1–6 (2020). https://doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041089

  23. Karimian, N., Woodard, D., Forte, D.: ECG biometric: spoofing and countermeasures. IEEE Trans. Biom. Behav. Identity Sci. 2, 257–270 (2020). https://doi.org/10.1109/TBIOM.2020.2992274

    Article  Google Scholar 

  24. Mondal, S., Bours, P.: Swipe gesture based continuous authentication for mobile devices. In: 2015 International Conference on Biometrics (ICB), pp. 458–465 (2015). https://doi.org/10.1109/ICB.2015.7139110

  25. Mowla, N., Doh, I., Chae, K.: Selective fuzzy ensemble learner for cognitive detection of bio-identifiable modality spoofing in MCPS. In: 2018 20th International Conference on Advanced Communication Technology (ICACT), pp. 63–67 (2018). https://doi.org/10.23919/ICACT.2018.8323646

  26. Vareto, R.H., Saldanha, A.M., Schwartz, W.R.: The swax benchmark: attacking biometric systems with wax figures. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 986–990 (2020). https://doi.org/10.1109/ICASSP40776.2020.9053946

  27. Lee, C.E., Zheng, L., Zhang, Y., Thing, V.L.L., Chu, Y.Y.: Towards building a remote anti-spoofing face authentication system. In: TENCON 2018 - 2018 IEEE Region 10 Conference, pp. 0321–0326 (2018). https://doi.org/10.1109/TENCON.2018.8650440

  28. Matthew, P., Anderson, M.: Developing coercion detection solutions for biometrie security. In: 2016 SAI Computing Conference (SAI), pp. 1123–1130 (2016). https://doi.org/10.1109/SAI.2016.7556118

  29. Wu, L., Yang, J., Zhou, M., Chen, Y., Wang, Q.: LVID: a multimodal biometrics authentication system on smartphones. IEEE Trans. Inf. Forensics Secur. 15, 1572–1585 (2020). https://doi.org/10.1109/TIFS.2019.2944058

    Article  Google Scholar 

  30. Aware Logo: Liveness detection in biometrics is essential for mobile authentication and onboarding. Aware (2019). https://www.aware.com/blog-liveness-detection-mobile-authentication-onboarding/. Accessed 12 Feb 2021

  31. Qayyum, A.B.A., Arefeen, A., Shahnaz, C.: Convolutional neural network (CNN) based speech-emotion recognition. In: 2019 IEEE International Conference on Signal Processing, Information, Communication Systems (SPICSCON), pp. 122–125 (2019). https://doi.org/10.1109/SPICSCON48833.2019.9065172

  32. Lou, G., Shi, H.: Face image recognition based on convolutional neural network. China Commun. 17, 117–124 (2020). https://doi.org/10.23919/JCC.2020.02.010

    Article  Google Scholar 

  33. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943). https://doi.org/10.1007/BF02478259

    Article  MathSciNet  MATH  Google Scholar 

  34. Farley, B., Clark, W.: Simulation of self-organizing systems by digital computer. Trans. IRE Prof. Group Inf. Theory 4, 76–84 (1954). https://doi.org/10.1109/TIT.1954.1057468

    Article  MathSciNet  Google Scholar 

  35. Ahmed, T., Das, P., Ali, M.F., Mahmud, M.-F.: A comparative study on convolutional neural network based face recognition. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–5 (2020). https://doi.org/10.1109/ICCCNT49239.2020.9225688

  36. Hebb, D.O.: The Organization of Behavior: A Neuropsychological Theory. Psychology Press, New York (2005)

    Book  Google Scholar 

  37. Qin, Z., Huang, G., Xiong, H., Qin, Z., Choo, K.-K.R.: A fuzzy authentication system based on neural network learning and extreme value statistics. IEEE Trans. Fuzzy Syst. 29, 549–559 (2021). https://doi.org/10.1109/TFUZZ.2019.2956896

    Article  Google Scholar 

  38. Kuznetsov, A., Oleshko, I., Chernov, K., Bagmut, M., Smirnova, T.: Biometric authentication using convolutional neural networks. In: Ilchenko, M., Uryvsky, L., Globa, L. (eds.) MCT 2019. LNNS, vol. 152, pp. 85–98. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-58359-0_6

    Chapter  Google Scholar 

  39. Pinto, A., Schwartz, W.R., Pedrini, H., de Rezende Rocha, A.: Using visual rhythms for detecting video-based facial spoof attacks. IEEE Trans. Inf. Forensics Secur. 10, 1025–1038 (2015). https://doi.org/10.1109/TIFS.2015.2395139

    Article  Google Scholar 

  40. Rattani, A., Scheirer, W.J., Ross, A.: Open set fingerprint spoof detection across novel fabrication materials. IEEE Trans. Inf. Forensics Secur. 10, 2447–2460 (2015). https://doi.org/10.1109/TIFS.2015.2464772

    Article  Google Scholar 

  41. Patel, K., Han, H., Jain, A.K.: Secure face unlock: spoof detection on smartphones. IEEE Trans. Inf. Forensics Secur. 11, 2268–2283 (2016). https://doi.org/10.1109/TIFS.2016.2578288

    Article  Google Scholar 

  42. Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensics Secur. 10, 746–761 (2015). https://doi.org/10.1109/TIFS.2015.2400395

    Article  Google Scholar 

  43. Nikisins, O., Mohammadi, A., Anjos, A., Marcel, S.: On effectiveness of anomaly detection approaches against unseen presentation attacks in face anti-spoofing. In: 2018 International Conference on Biometrics (ICB), pp. 75–81 (2018). https://doi.org/10.1109/ICB2018.2018.00022

  44. Pinto, A., Pedrini, H., Schwartz, W.R., Rocha, A.: Face spoofing detection through visual codebooks of spectral temporal cubes. IEEE Trans. Image Process. 24, 4726–4740 (2015). https://doi.org/10.1109/TIP.2015.2466088

    Article  MathSciNet  MATH  Google Scholar 

  45. Rathgeb, C., Wagner, J., Tams, B., Busch, C.: Preventing the cross-matching attack in Bloom filter-based cancelable biometrics. In: 3rd International Workshop on Biometrics and Forensics (IWBF 2015), pp. 1–6 (2015). https://doi.org/10.1109/IWBF.2015.7110226

  46. Steiner, H., Kolb, A., Jung, N.: Reliable face anti-spoofing using multispectral SWIR imaging. In: 2016 International Conference on Biometrics (ICB), pp. 1–8 (2016). https://doi.org/10.1109/ICB.2016.7550052

  47. Chugh, T., Jain, A.K.: Fingerprint spoof detector generalization. IEEE Trans. Inf. Forensics Secur. 16, 42–55 (2021). https://doi.org/10.1109/TIFS.2020.2990789

    Article  Google Scholar 

  48. Edwards, T., Hossain, M.S.: Effectiveness of deep learning on serial fusion based biometric systems. IEEE Trans. Artif. Intell. 1 (2021). https://doi.org/10.1109/TAI.2021.3064003

  49. IEEE Standard for Biometric Liveness Detection. IEEE Std 2790-2020, pp. 1–24 (2020). https://doi.org/10.1109/IEEESTD.2020.9080669

  50. Babu, A., Paul, V., Baby, D.E.: An investigation of biometric liveness detection using various techniques. In: 2017 International Conference on Inventive Systems and Control (ICISC), pp. 1–5 (2017). https://doi.org/10.1109/ICISC.2017.8068745

  51. Okereafor, K., Onime, C., Osuagwu, O.: Enhancing biometric liveness detection using trait randomization technique. In: 2017 UKSim-AMSS 19th International Conference on Computer Modelling Simulation (UKSim), pp. 28–33 (2017). https://doi.org/10.1109/UKSim.2017.44

  52. Garg, S., Mittal, S., Kumar, P., Athavale, V.A.: DeBNet: multilayer deep network for liveness detection in face recognition system. In: 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 1136–1141 (2020). https://doi.org/10.1109/SPIN48934.2020.9070853

  53. Liveness Detection with OpenCV. PyImageSearch (2019). https://www.pyimagesearch.com/2019/03/11/liveness-detection-with-opencv/. Accessed 12 Feb 2021

  54. Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: 2012 BIOSIG - Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG), pp. 1–7 (2012)

    Google Scholar 

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Authors and Affiliations

  1. V. N. Karazin Kharkiv National University, Svobody sq., 4, Kharkiv, 61022, Ukraine

    Alexandr Kuznetsov, Sergey Fedotov & Mykhaylo Bagmut

  2. JSC “Institute of Information Technologies”, Bakulin St., 12, Kharkiv, 61166, Ukraine

    Alexandr Kuznetsov

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  1. Alexandr Kuznetsov
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  2. Sergey Fedotov
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  3. Mykhaylo Bagmut
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Correspondence to Alexandr Kuznetsov .

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  1. University of Gdansk, Sopot, Poland

    Stanisław Wrycza

  2. University of Gdansk, Sopot, Poland

    Jacek Maślankowski

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Kuznetsov, A., Fedotov, S., Bagmut, M. (2021). Convolutional Neural Networks to Protect Against Spoofing Attacks on Biometric Face Authentication. In: Wrycza, S., Maślankowski, J. (eds) Digital Transformation. PLAIS EuroSymposium 2021. Lecture Notes in Business Information Processing, vol 429. Springer, Cham. https://doi.org/10.1007/978-3-030-85893-3_9

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