Abstract
The wide deployment of face recognition systems has raised serious concern on its security level. One of the most prominent threats is the presentation attack, which attempts to fool the system in the absence of the real person. In this article, we propose a way of identifying the spoof face by using spoofing noise. Since the spoofing noise is brought into the spoof image when an adversary uses some tricks to fool the face recognition system, it consists of imagining device noise and is affected by the spoofing environments. We think it is a clue against fake face. We first address how to use color channel difference images to enhance the spoofing noise, and then introduce a self-adapting attention framework named Noise-Attention Network to learn the end-to-end spoofing-features. Experiments on benchmarks including CASIA-FASD, MSU-MFSD and Idiap Replay-Attack demonstrate the effectiveness of the proposed method. It can yield results comparable with other current methods but has better robustness.
Supported in part by Sino-Singapore International Joint Research Institute (No. 206-A017023, No. 206-A018001), and Science and Technology Foundation of Guangzhou Huangpu Development District under Grant 201902010028.
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References
Zinelabidine B., Jukka K., Abdenour H.: Face anti-spoofing based on color texture analysis. In: International Conference on Image Processing (ICIP), pp. 2636–2640. IEEE, Quebec City (2015)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on color texture analysis. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 2636–2640. IEEE, Quebec City (2015)
Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.Z.: A face anti-spoofing database with diverse attacks. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 26–31. IEEE, New Delhi (2012)
Atoum, Y., Liu, Y., Jourabloo, A., Liu, X.: Face anti-spoofing using patch and depth-based CNNs. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 319–328. IEEE, Denver (2017)
Xu, Z., Li, S., Deng, W.: Learning temporal features using LSTM-CNN architecture for face anti-spoofing. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 141–145. IEEE, Kuala Lumpur (2015)
Jourabloo, A., Liu, Y., Liu, X.: Face de-spoofing: anti-spoofing via noise modeling. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 297–315. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_18
Park, J., Woo, S., Lee, J., Kweon, I.: BAM: bottleneck attention module. In: Proceedings of British Machine Vision Conference (BMVC), Newcastle, UK, p. 147 (2018)
Ma, B., Wang, X., Zhang, H., Li, F., Dan, J.: CBAM-GAN: generative adversarial networks based on convolutional block attention module. In: Sun, X., Pan, Z., Bertino, E. (eds.) ICAIS 2019. LNCS, vol. 11632, pp. 227–236. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24274-9_20
Klein, T., Nabi, M.: Attention is (not) all you need for commonsense reasoning. arXiv preprint arXiv:1905.13497(2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE, Las Vegas (2016)
de Freitas Pereira, T., Anjos, A., De Martino, J. M., Marcel, S.: Can face anti-spoofing countermeasures work in a real world scenarion. In: 2013 International Conference on Biometrics (ICB), pp. 1–8. IEEE, Madrid (2013)
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. IEEE, Salt Lake City (2018)
Chen, H., Hu, G., Lei, Z., Chen, Y., Robertson, N.M., Li, S.Z.: Attention-based two-stream convolutional networks for face spoofing detection. IEEE Trans. Inf. Forensics Secur. 15, 578–593 (2019)
Menotti, D., et al.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 864–879 (2015)
Lucena, O., Junior, A., Moia, V., et al.: Transfer learning using convolutional neural networks for face anti-spoofing. Proceedings of the 14th International Conference on Image Analysis and Recognition (ICIAR), Montreal, Canada, pp. 27–34 (2017)
Li, H., Li, W., Cao, H., Wang, S., Huang, F., Kot, A.C.: Unsupervised domain adaptation for face anti-spooing. IEEE Trans. Inf. Forensics Secur. 13, 1794–1809 (2018)
Pinto, A., Pedrini, H., Schwartz, W.R., Rocha, A.: Face spoofing detection through visual codebooks of spectral temporal cubes. IEEE Trans. Image Process 24, 4726–4740 (2015)
Yang, J., Lei, Z., Li, S.Z.: Learn convolutional neural network for face anti-spoofing. CoRR, vol. abs/1408.5601, August 2018. http://arxiv.org/abs/1408.5601
Benlamoudi, A., Aiadi, K.E., Ouafi, A., et al.: Face antispoofing based on frame difference and multilevel representation. J. Electron. Imaging 26(4), 043007 (2017)
Di, W., Hu, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensics Secur. 10(4), 746–761 (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, pp. 3104–3112 (2014)
Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing(EMNLP), Lisbon, Portugal, pp. 17–21 (2015)
Sun, L., Pan, G., Wu, Z., Lao, S.: Blinking-based live face detection using conditional random fields. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 252–260. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_27
Pan, G., Sun, L., Wu, Z., et al.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: Proceedings of the 2007 IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, pp. 1–8. IEEE (2007)
Gragnaniello, D., Poggi, G., Sansone, C., et al.: An investigation of local descriptors for biometric spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 849–863 (2015)
Mirjalili, V., Ross, A.: Soft biometric privacy: retaining biometric utility of face images while perturbing gender. In: 2017 IEEE International Conference on Biometrics (IJCB), pp. 564–573. IEEE, CO, USA (2017)
Li, H., He, P., Wang, S., et al.: Learning generalized deep feature representation for face anti-spoofing. IEEE Trans. Inf. Forensics Secur. 13, 2639–2652 (2018)
Komulainen, J., Hadid, A., Pietikainen, M.: Context based face anti-spoofing. In: IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems, Arlington, US, pp. 1–8 (2013)
Yang, J., Lei, Z., Liao, S., Li, S.Z.: Face liveness detection with component dependent descriptor. In: IEEE International Conference on Biometrics, Madrid, Spain, pp. 1–6 (2013)
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Ren, Y., Hu, Y., Liu, B., Xie, Y., Wang, Y. (2020). Face Anti-spoofing with a Noise-Attention Network Using Color-Channel Difference Images. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_41
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