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Detecting Face Morphing Attacks with Collaborative Representation of Steerable Features

  • Raghavendra RamachandraEmail author
  • Sushma Venkatesh
  • Kiran Raja
  • Christoph Busch
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1022)

Abstract

Passports have used face characteristics to verify and establish the identity of an individual. Face images provide high accuracy in verification and also present the opportunity of verifying the identity visually against the passport face image if the need arises. Morphed image-based identity attacks are recently shown to exploit the vulnerability of passport issuance and verification systems, where two different identities are morphed into one image to match against both images. The challenge is further increased when the properties in the digital domain are lost after the process of print and scan. This work addresses such a problem of detecting the morphing of face images such that the attacks are detected even after the print and scan process. As the first contribution of this work, we extend an existing database with 693 bonafide and 1202 morphed face images with the newly added of 579 bonafide and 1315 morphed images. We further propose a new approach based on extracting textural features across scale-space and classifying them using collaborative representation. With a set of extensive experiments and benchmarking against the traditional (non-deep-learning methods) and deep-learning methods, we illustrate the applicability of the proposed approach in detecting the morphing attacks. With an obtained Bonafide Presentation Classification Error (BPCER) of \(13.12\%\) at Attack Presentation Classification Error Rate (APCER) of \(10\%\), the use of the proposed method can be envisioned for detecting morph attacks even after print and scan process.

Keywords

Biometrics Face morphing Spoofing attacks 

Notes

Acknowledgements

This work was carried out under the funding of the Research Council of Norway under Grant No. IKTPLUSS 248030/O70.

References

  1. 1.
    Asaad, A., Jassim, S.: Topological data analysis for image tampering detection. In: International Workshop on Digital Watermarking, pp. 136–146 (2017)Google Scholar
  2. 2.
    Ferrara, M., Franco, A., Maltoni, D.: The magic passport. In: IEEE International Joint Conference on Biometrics, pp. 1–7 (2014)Google Scholar
  3. 3.
    Ferrara, M., Franco, A., Maltoni, D.: Face recognition across the imaging spectrum. In: On the Effects of Image Alterations on Face Recognition Accuracy, pp. 195–222. Springer International Publishing (2016)Google Scholar
  4. 4.
    Ferrara, M., Franco, A., Maltoni, D.: Face demorphing. IEEE Trans. Inf. Forensics Secur. 13(4), 1008–1017 (2018)CrossRefGoogle Scholar
  5. 5.
    Freeman, W.T., Adelson, E.H., et al.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13(9), 891–906 (1991)CrossRefGoogle Scholar
  6. 6.
    Hildebrandt, M., Neubert, T., Makrushin, A., Dittmann, J.: Benchmarking face morphing forgery detection: application of stirtrace for impact simulation of different processing steps. In: International Workshop on Biometrics and Forensics (IWBF 2017), pp. 1–6 (2017)Google Scholar
  7. 7.
    International Organization for Standardization: Information Technology—Biometric Presentation Attack Detection—Part 3: Testing and Reporting. ISO/IEC DIS 30107-3:2016, JTC 1/SC 37, Geneva, Switzerland (2016)Google Scholar
  8. 8.
    Makrushin, A., Neubert, T., Dittmann, J.: Automatic generation and detection of visually faultless facial morphs. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications—vol. 6: VISAPP, (VISIGRAPP 2017), pp. 39–50 (2017)Google Scholar
  9. 9.
    Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Neubert, T.: Face morphing detection: an approach based on image degradation analysis. In: International Workshop on Digital Watermarking, pp. 93–106 (2017)Google Scholar
  11. 11.
    Poynton, C.: Digital Video and HD: Algorithms and Interfaces. Elsevier (2012)Google Scholar
  12. 12.
    Raghavendra, R., Raja, K.B., Busch, C.: Detecting morphed face images. In: 8th IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS), pp. 1–8 (2016)Google Scholar
  13. 13.
    Raghavendra, R., Raja, K.B., Venkatesh, S., Busch, C.: Transferable deep-CNN features for detecting digital and print-scanned morphed face images. In: Proceedings of the IEEE Conference on Computer Vision Pattern Recognition Workshops (CVPRW), pp. 1822–1830 (2017)Google Scholar
  14. 14.
    Raghavendra, R., Raja, K., Venkatesh, S., Busch, C.: Face morphing versus face averaging: vulnerability and detection. In: IEEE International Joint Conference on Biometrics (IJCB), pp. 555–563 (2017)Google Scholar
  15. 15.
    Robertson, D., Kramer, R.S., Burton, A.M.: Fraudulent id using face morphs: experiments on human and automatic recognition. PLoS ONE 12(3), 1–12 (2017)Google Scholar
  16. 16.
    Scherhag, U., Raghavendra, R., Raja, K., Gomez-Barrero, M., Rathgeb, C., Busch, C.: On the vulnerability of face recognition systems towards morphed face attack. In: International Workshop on Biometrics and Forensics (IWBF 2017), pp. 1–6 (2017)Google Scholar
  17. 17.
    Seibold, C., Samek, W., Hilsmann, A., Eisert, P.: Detection of face morphing attacks by deep learning. In: International Workshop on Digital Watermarking, pp. 107–120 (2017)Google Scholar
  18. 18.
    Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition? In: IEEE International Conference on Computer Vision (ICCV), pp. 471–478 (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Raghavendra Ramachandra
    • 1
    Email author
  • Sushma Venkatesh
    • 1
  • Kiran Raja
    • 1
  • Christoph Busch
    • 1
  1. 1.Norwegian University of Science and Technology (NTNU)GjøvikNorway

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