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A Deep Learning Technique to Countermeasure Video-Based Presentation Attacks

  • Thomas DannerEmail author
  • Prosenjit Chatterjee
  • Kaushik Roy
Conference paper
  • 61 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

体育赛事投注记录presentation attack detection (pad) on faces is crucial to countermeasure face recognition system from a security breach. the increase in convenience to our lives is not without its own additional avenue for exposure to a security breach. the presentation attack (pa), or spoofing attack is the act of using artificial/synthetic materials to get unauthorized access into a secured system. for example, an individual could use a 3d printed mask, or even a digital or printed photograph to circumvent a facial recognition authentication system, for the case of iris-detection an intruder could use custom contact lenses, and for fingerprints, digital prosthetics to penetrate a secured system. previously, many deep learning approaches utilize a very deep complex model to handle face pad. to countermeasure the above issue, in this paper, we apply a deep learning approach to mitigate the presentation attack. in our proposed approach, we implement a lightweight ‘modified-alexnet’ and obtained the highest test accuracy of 99.89% on the spoof in the wild (siw) dataset.

Notes

Acknowledgements

we would like to acknowledge the support from the national science foundation (nsf).

References

  1. 1.
    Galbally, J., Marcel, S.: Face anti-spoofing based on general image quality assessment. In: 2014 22nd International Conference on Pattern Recognition, pp. 1173–1178. IEEE (2014)
  2. 2.
    Schwartz, W.R., Rocha, A., Pedrini, H.: Face spoofing detection through partial least squares and low-level descriptors. In: IEEE International Joint Conference on Biometrics (IJCB), pp. 1–8 (2011)
  3. 3.
    Menotti, D., et al.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 864–879 (2015).  
  4. 4.
    Freitas Pereira, T., Komulainen, J., Anjos, A., De Martino, J., Hadid, A., Pietikainen, M., Marcel, S.: Face liveness detection using dynamic texture. EURASIP J. Image Video Process. (1), 2 (2014)
  5. 5.
    Pinto, A., Pedrini, H., Schwartz, W.R., Rocha, A.: Video-based face spoofing detection through visual rhythm analysis. In: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 221–228 (2012)
  6. 6.
    Yang, J., Lei, Z., Li., S.: Learn convolutional neural network for face anti-spoofing. [cs.CV] (2014)
  7. 7.
    Nguyen, D., Pham, T., Baek, N., Park, K.: Combining deep and handcrafted image features for presentation attack detection in face recognition systems using visible-light camera sensors. Sensors 18(3) (2018)
  8. 8.
    Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: Binary or auxiliary supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 389–398 (2018)
  9. 9.
    Liu, Y., Jourabloo, A., Liu X.: Spoof in the wild (SiW) face anti-spoofing database
  10. 10.
    Wen, D.: Mobile face spoofing database (CASIA-MFSD) database (2014)
  11. 11.
    Marcel, S., Anjos, A.: Replay-attack database (2012)

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Thomas Danner
    • 1
    Email author
  • Prosenjit Chatterjee
    • 1
  • Kaushik Roy
    • 1
  1. 1.Department of Computer ScienceNorth Carolina A & T State UniversityGreensboroUSA

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