Abstract
Face anti-spoofing based on deep learning achieved good accuracy recently. However, deep learning model has no explicit mathematical presentation. Therefore, it is not clear about how the model works effectively. In this paper, we estimate the regions in face image, which are sensitive in deep learning based anti-spoofing algorithms. We first generate the adversarial examples from two different gradient-based methods. Then we analyze the distribution of the gradient and perturbations on the adversarial examples. And next we obtain the sensitive regions and evaluate the contribution of these regions to classification performance. By analyzing the sensitive regions, it could be observed that the CNN based anti-spoofing algorithms are sensitive to rich detailed regions and illumination. These observations are helpful to design an effective face anti-spoofing algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. IEEE Trans. Inf. Forensics Secur. 11(8), 1818–1830 (2017)
Maatta, J., Hadid, A., Pietikainen, M.: Face spoofing detection from single images using texture and local shape analysis. IET Biometrics 1(1), 3–10 (2012)
Wu, L., Xu, Y., Jian, M., Cai, W., Yan, C., Ma, Y.: Motion analysis based cross-database voting for face spoofing detection. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 528–536. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69923-3_57
Li, L., Feng, X., Boulkenafet, Z., et al.: An original face anti-spoofing approach using partial convolutional neural network. In: International Conference on Image Processing Theory TOOLS and Applications, pp. 1–6. IEEE (2017)
Lucena, O., Junior, A., Moia, V., Souza, R., Valle, E., Lotufo, R.: Transfer learning using convolutional neural networks for face anti-spoofing. In: Karray, F., Campilho, A., Cheriet, F. (eds.) ICIAR 2017. LNCS, vol. 10317, pp. 27–34. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59876-5_4
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I.J., Fergus, R.: Intriguing properties of neural networks. In: ICLR, abs/1312.6199 (2014)
Yin, B., Wang, W., Wang, L.: Review of Deep Learning. J. Beijing Univ. Technol. 1, 48–59 (2015)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Readings Cogn. Sci. 323(6088), 399–421 (1988)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICML, pp. 1–10 (2015)
Moosavidezfooli, S.M., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. Computer Vision and Pattern Recognition. IEEE, pp. 2574–2582 (2016)
Ma, Y., Wu, L., Jian, M., Liu, F., Yang, Z.: Approach to generate adversarial examples for face-spoofing detection. J. Softw. http://www.jos.org.cn/jos/ch/reader/view_abstract.aspx?file_no=5568
Anjos, A., Marcel, S.: Counter-measures to photo attacks in face recognition: a public database and a baseline. In: International Joint Conference on Biometrics, pp. 1–7. IEEE (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Ma, Y., Wu, L., Jian, M. (2018). Sensitive Information of Deep Learning Based Face Anti-spoofing Algorithms. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-97909-0_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97908-3
Online ISBN: 978-3-319-97909-0
eBook Packages: Computer ScienceComputer Science (R0)