Skip to main content

Anti-spoofing, Evaluation Methodologies

  • Reference work entry
  • First Online:
Encyclopedia of Biometrics

Synonyms

Performance Measures; Spoofability; Vulnerability assessment

Definition

Following the definition of the task of the anti-spoofing systems to discriminate between real accesses and spoofing attacks, anti-spoofing can be regarded as a binary classification problem. The spoofing databases and the evaluation methodologies for anti-spoofing systems most often comply to the standards for binary classification problems. However, the anti-spoofing systems are not destined to work stand-alone, and their main purpose is to protect a verification system from spoofing attacks. In the process of combining the decision of an anti-spoofing and a recognition system, effects on the recognition performance can be expected. Therefore, it is important to analyze the problem of anti-spoofing under the umbrella of biometric recognition systems. This brings certain requirements in the database design, as well as adapted concepts for evaluation of biometric recognition systems under spoofing attacks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 899.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. E. Marasco, Y. Ding, A. Ross, Combining match scores with liveness values in a fingerprint verification system, in 5th IEEE International Conference on Biometrics: Theory, Applications and Systems, Arlington, 2012

    Google Scholar 

  2. J. Galbally, F. Alonso-Fernandez, J. Fierrez, J. Ortega-Garcia, A high performance fingerprint liveness detection method based on quality related features. Future Gener. Comput. Syst. 28(1), 311–321 (2012)

    Google Scholar 

  3. G. Pan, L. Sun, Z. Wu, S. Lao, Eyeblink-based anti-spoofing in face recognition from a generic webcamera, in IEEE 11th International Conference on Computer Vision, ICCV 2007, Rio de Janeiro, 2007, pp. 1–8

    Google Scholar 

  4. W. Bao, H. Li, N. Li, W. Jiang, A liveness detection method for face recognition based on optical flow field, in International Conference on Image Analysis and Signal Processing, IASP 2009, Linhai, 2009, pp. 233–236

    Google Scholar 

  5. P. Johnson, R. Lazarick, E. Marasco, E. Newton, A. Ross, S. Schuckers, Biometric liveness detection: framework and metrics, in International Biometric Performance Conference, 2012

    Google Scholar 

  6. N. Poh, S. Bengio, Database, protocols and tools for evaluating score-level fusion algorithms in biometric authentication. Pattern Recognit. J. 39, 223–233 (2006)

    Google Scholar 

  7. A. Martin, G. Doddington, T. Kamm, M.M. Ordowski, The DET curve in assessment of detection task performance, in Eurospeech, Rhodes, 1997, pp. 1895–1898

    Google Scholar 

  8. S. Bengio, J. Mariéthoz, M. Keller, The expected performance curve, in International Conference on Machine Learning, ICML, Workshop on ROC Analysis in Machine Learning, 2005

    Google Scholar 

  9. P.A. Johnson, B. Tan, S. Schuckers, Multimodal fusion vulnerability to non-zero (spoof) imposters, in IEEE International Workshop on Information Forensics and Security, Seattle, 2010

    Google Scholar 

  10. J. Galbally, R. Cappelli, A. Lumini, G.G. de Rivera, D. Maltoni, J. Fiérrez, J. Ortega-Garcia, D. Maio, An evaluation of direct attacks using fake fingers generated from iso templates. Pattern Recognit. Lett. 31(8), 725–732 (2010)

    Google Scholar 

  11. J. Galbally-Herrero, J. Fierrez-Aguilar, J.D. Rodriguez-Gonzalez, F. Alonso-Fernandez, J. Ortega-Garcia, M. Tapiador, On the vulnerability of fingerprint verification systems to fake fingerprints attacks, in IEEE International Carnahan Conference on Security Technology, Lexington, 2006, pp. 169–179

    Google Scholar 

  12. A. Adler, S. Schuckers, Security and liveness, overview, in Encyclopedia of Biometrics, ed. by S.Z. Li, A.K. Jain (Springer, New York, 2009), pp. 1146–1152

    Google Scholar 

  13. R. Rodrigues, N. Kamat, V. Govindaraju, Evaluation of biometric spoofing in a multimodal system, in 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), Washington, DC, 2010

    Google Scholar 

  14. J.-F. Bonastre, D. Matrouf, C. Fredouille, Artificial impostor voice transformation effects on false acceptance rates, in INTERSPEECH, Antwerp, 2007, pp. 2053–2056

    Google Scholar 

  15. V. Ruiz-Albacete, P. Tome-Gonzalez, F. Alonso-Fernandez, J. Galbally, J. Fierrez, J. Ortega-Garcia, Direct attacks using fake images in iris verification, in Proceedings of COST 2101 Workshop on Biometrics and Identity Management, BIOID, Roskilde (Springer, 2008), pp. 181–190

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this entry

Cite this entry

Chingovska, I., Anjos, A., Marcel, S. (2015). Anti-spoofing, Evaluation Methodologies. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_9212

Download citation

Publish with us

Policies and ethics