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access icon free Predicting the vulnerability of biometric systems to attacks based on morphed biometric information

Morphing techniques can be used to create artificial biometric samples or templates, which resemble the biometric information of two or more individuals in signal and feature domain. If morphed biometric samples or templates are infiltrated to a biometric recognition system, the subjects contributing to the morphed sample can be both successfully verified against a single enrolled template. Hence, the unique link between individuals and their biometric reference data is not warranted. This leads to serious security gaps in biometric applications, in particular, the issuance and verification process of electronic travel documents. Recently, different biometric systems have been attacked using morphed biometric samples. However, so far a systematic approach to predict the vulnerability of the system to such attacks has not been proposed. In this work, the authors present a framework to evaluate the vulnerability of biometric systems to attacks using morphed biometric information. Based on a biometric system's mated/non-mated score distribution and its decision threshold, a theoretical vulnerability assessment is proposed. In an experimental evaluation, the vulnerability of a face and an iris recognition system is quantified based on the presented framework. Obtained results are verified against real attacks based on morphed face images and morphed iris-based templates.

References

    1. 1)
      • 1. ISO/IEC TC JTC1 SC37 Biometrics, ISO/IEC 2382-37:2017 IT – Vocabulary – Part 37: Biometrics, ISO and IEC, 2017.
    2. 2)
      • 16. Raghavendra, R., Raja, K., Venkatesh, S., et al: ‘Transferable deep-CNN features for detecting digital and print-scanned morphed face images’. 2017 IEEE Conf. on Computer Vision and Pattern Recognition Workshop (CVPRW), July 2017.
    3. 3)
      • 25. Chinese Academy of Sciences’ Institute of Automation.: ‘CASIA Iris Image Database V4.0 – Interval’, 2010, http://biometrics.idealtest.org.
    4. 4)
      • 15. Ferrara, M., Franco, A., Maltoni, D.: ‘On the effects of image alterations on face recognition accuracy’, in: Bourlai, T. (Ed.): ‘Face recognition across the imaging spectrum’ (Springer International Publishing, 2016), pp. 195222.
    5. 5)
      • 4. ISO/IEC TC JTC1 SC37 Biometrics, ISO/IEC IS 30107-1. Information Technology – Biometrics presentation attack detection – Part 1: Framework, International Organization for Standardization, Mar. 2016.
    6. 6)
      • 18. Raghavendra, R., Raja, K., Venkatesh, S., et al: ‘Face morphing versus face averaging: vulnerability and detection’. Proc. Int. Joint Conf. on Biometrics (IJCB), 2017.
    7. 7)
      • 13. Scherhag, U., Nautsch, A., Rathgeb, C., et al: ‘Biometric systems under morphing attacks: assessment of morphing techniques and vulnerability reporting’. Int. Conf. of the Biometrics Special Interest Group (BIOSIG), 2017, pp. 112.
    8. 8)
      • 23. ISO/IEC JTC1 SC37 Biometrics, ISO/IEC FDIS 30107-3:2017, IT – Biometric presentation attack detection – Part 3: Testing and Reporting.
    9. 9)
      • 6. Scherhag, U., Raghavendra, R., Raja, K.B., et al: ‘On the vulnerability of face recognition systems towards morphed face attacks’. Proc. Int. Workshop on Biometrics and Forensics (IWBF), 2017, pp. 16.
    10. 10)
      • 27. King, D.E.: ‘Dlib-ml: a machine learning toolkit’, J. Mach. Learn. Res., 2009, 10, pp. 17551758.
    11. 11)
      • 19. Agarwal, A., Singh, R., Vatsa, M., et al: ‘SWAPPED! digital face presentation attack detection via weighted local magnitude pattern’. Proc. Int. Joint Conf. on Biometrics (IJCB), 2017.
    12. 12)
      • 32. Delaunay, B.: ‘Sur la sphère vide. a la mémoire de george voronoi’, Bull. Acad. Sci. URSS, Class. Sci. Math. Nat., 1934, 6, pp. 793800.
    13. 13)
      • 28. Ma, L., Tan, T., Wang, Y., et al: ‘Efficient iris recognition by characterizing key local variations’, IEEE Trans. Image Process., 2004, 13, (6), pp. 739750.
    14. 14)
      • 30. Rathgeb, C., Uhl, A., Wild, P.: ‘Iris recognition: from segmentation to template security, ser. Advances in information security’ (Springer-Verlag, 2013), vol. 59.
    15. 15)
      • 3. Marcel, S., Nixon, M., Li, S. Z.: ‘Handbook of biometric anti-spoofing’ (Springer-Verlag, New York, 2014).
    16. 16)
      • 2. Ratha, N.K., Connell, J.H., Bolle, R.M.: ‘Enhancing security and privacy in biometrics-based authentication systems’, IBM Syst. J., 2001, 40, (3), pp. 614634.
    17. 17)
      • 10. Gomez-Barrero, M., Rathgeb, C., Scherhag, U., et al: ‘Is your biometric system robust to morphing attacks?’. Proc. Int. Workshop on Biometrics and Forensics (IWBF), 2017, pp. 16.
    18. 18)
      • 8. Hildebrandt, M., Neubert, T., Makrushin, A., et al: ‘Benchmarking face morphing forgery detection: application of stirtrace for impact simulation of different processing steps’. Proc. Int. Workshop on Biometrics and Forensics (IWBF), 2017, pp. 16.
    19. 19)
      • 33. Porter, T., Duff, T.: ‘Compositing digital images’, Comput. Graph., 1984, 18, (3), pp. 253259.
    20. 20)
      • 12. Kraetzer, C., Makrushin, A., Neubert, T., et al: ‘Modeling attacks on photo-ID documents and applying media forensics for the detection of facial morphing’. Proc. Workshop on Information Hiding and Multimedia Security (IH & MMSec), 2017, pp. 2132.
    21. 21)
      • 24. Martinez, A.: ‘The AR face database’, CVC Tech. Report, Tech. Rep., 1998.
    22. 22)
      • 29. ‘USIT – University of Salzburg iris toolkit’, http://www.wavelab.at/sources/Rathgeb16a, version 2.0.x.
    23. 23)
      • 14. ‘FRONTEX – Research and Development Unit: Best practice technical guidelines for automated border control (ABC) systems,’2012, version 2.0.
    24. 24)
      • 11. Raghavendra, R., Raja, K.B., Busch, C.: ‘Detecting morphed face images’. Proc. Int. Conf. on Biometrics: Theory, Applications, and Systems (BTAS), 2016.
    25. 25)
      • 31. Daugman, J.: ‘Probing the uniqueness and randomness of iris codes: results from 200 billion iris pair comparisons’, Proc. IEEE, 2006, 94, (11), pp. 19271935.
    26. 26)
      • 22. Wandzik, L., Garcia, R.V., Kaeding, G., et al: ‘CNNs under attack: on the vulnerability of deep neural networks based face recognition to image morphing’. Proc. Int. Workshop on Digital Forensics and Watermarking (IWDW), 2017, pp. 121135.
    27. 27)
      • 26. Amos, B., Ludwiczuk, B., Satyanarayanan, M.: ‘OpenFace: a general-purpose face recognition library with mobile applications’, CMU School of Computer Science, Tech. Rep., 2016.
    28. 28)
      • 17. Seibold, C., Samek, W., Hilsmann, A., et al: ‘Detection of face morphing attacks by deep learning’. Proc. Int. Workshop on Digital Forensics and Watermarking (IWDW), 2017, pp. 107120.
    29. 29)
      • 5. Ferrara, M., Franco, A., Maltoni, D.: ‘The magic passport’. Proc. Int. Joint Conf. on Biometrics (IJCB), 2014, pp. 17.
    30. 30)
      • 21. Neubert, T.: ‘Face morphing detection: an approach based on image degradation analysis’, Proc. Int. Workshop on Digital Forensics and Watermarking (IWDW), 2017, pp. 93106.
    31. 31)
      • 9. Rathgeb, C., Busch, C.: ‘On the feasibility of creating morphed iris-codes’. Proc. Int. Joint Conf. on Biometrics (IJCB), 2017, pp. 16.
    32. 32)
      • 20. Makrushin, A., Neubert, T., Dittmann, J.: ‘Automatic generation and detection of visually faultless facial morphs’. Proc. Int. Joint Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), 2017, pp. 3950.
    33. 33)
      • 7. Ferrara, M., Cappelli, R., Maltoni, D.: ‘On the feasibility of creating double identity fingerprints’, IEEE Trans. Inf. Forensics Sec., 2017, 12, (4), pp. 892900.
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