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

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

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.


    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)
      • 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.
    3. 3)
      • 3. Marcel, S., Nixon, M., Li, S. Z.: ‘Handbook of biometric anti-spoofing’ (Springer-Verlag, New York, 2014).
    4. 4)
      • 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.
    5. 5)
      • 5. Ferrara, M., Franco, A., Maltoni, D.: ‘The magic passport’. Proc. Int. Joint Conf. on Biometrics (IJCB), 2014, pp. 17.
    6. 6)
      • 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.
    7. 7)
      • 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.
    8. 8)
      • 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.
    9. 9)
      • 9. Rathgeb, C., Busch, C.: ‘On the feasibility of creating morphed iris-codes’. Proc. Int. Joint Conf. on Biometrics (IJCB), 2017, pp. 16.
    10. 10)
      • 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.
    11. 11)
      • 11. Raghavendra, R., Raja, K.B., Busch, C.: ‘Detecting morphed face images’. Proc. Int. Conf. on Biometrics: Theory, Applications, and Systems (BTAS), 2016.
    12. 12)
      • 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.
    13. 13)
      • 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.
    14. 14)
      • 14. ‘FRONTEX – Research and Development Unit: Best practice technical guidelines for automated border control (ABC) systems,’2012, version 2.0.
    15. 15)
      • 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.
    16. 16)
      • 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.
    17. 17)
      • 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.
    18. 18)
      • 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.
    19. 19)
      • 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.
    20. 20)
      • 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.
    21. 21)
      • 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.
    22. 22)
      • 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.
    23. 23)
      • 23. ISO/IEC JTC1 SC37 Biometrics, ISO/IEC FDIS 30107-3:2017, IT – Biometric presentation attack detection – Part 3: Testing and Reporting.
    24. 24)
      • 24. Martinez, A.: ‘The AR face database’, CVC Tech. Report, Tech. Rep., 1998.
    25. 25)
      • 25. Chinese Academy of Sciences’ Institute of Automation.: ‘CASIA Iris Image Database V4.0 – Interval’, 2010,
    26. 26)
      • 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.
    27. 27)
      • 27. King, D.E.: ‘Dlib-ml: a machine learning toolkit’, J. Mach. Learn. Res., 2009, 10, pp. 17551758.
    28. 28)
      • 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.
    29. 29)
      • 29. ‘USIT – University of Salzburg iris toolkit’,, version 2.0.x.
    30. 30)
      • 30. Rathgeb, C., Uhl, A., Wild, P.: ‘Iris recognition: from segmentation to template security, ser. Advances in information security’ (Springer-Verlag, 2013), vol. 59.
    31. 31)
      • 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.
    32. 32)
      • 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.
    33. 33)
      • 33. Porter, T., Duff, T.: ‘Compositing digital images’, Comput. Graph., 1984, 18, (3), pp. 253259.

Related content

This is a required field
Please enter a valid email address