Abstract
Prior studies show that the key to face anti-spoofing lies in the subtle image pattern, termed “spoof trace", e.g., color distortion, 3D mask edge, Moiré pattern, and many others. Designing a generic anti-spoofing model to estimate those spoof traces can improve both generalization and interpretability. Yet, this is a challenging task due to the diversity of spoof types and the lack of ground truth. This work designs a novel adversarial learning framework to disentangle the spoof traces from input faces as a hierarchical combination of patterns. With the disentangled spoof traces, we unveil the live counterpart from spoof face, and synthesize realistic new spoof faces after a proper geometric correction. Our method demonstrates superior spoof detection performance on both seen and unseen spoof scenarios while providing visually-convincing estimation of spoof traces. Code is available at https://github.com/yaojieliu/ECCV20-STDN.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
As most face recognition systems are based on a monocular camera, this work only concerns monocular face anti-spoofing methods, and terms as face anti-spoofing hereafter for simplicity.
References
Explainable Artificial Intelligence (XAI). https://www.darpa.mil/program/explainable-artificial-intelligence
IARPA research program Odin). https://www.iarpa.gov/index.php/research-programs/odin
Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020)
Atoum, Y., Liu, Y., Jourabloo, A., Liu, X.: Face anti-spoofing using patch and depth-based CNNs. In: IJCB. IEEE (2017)
Bigun, J., Fronthaler, H., Kollreider, K.: Assuring liveness in biometric identity authentication by real-time face tracking. In: International Conference on Computational Intelligence for Homeland Security and Personal Safety (CIHSPS). IEEE (2004)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on color texture analysis. In: ICIP. IEEE (2015)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face antispoofing using speeded-up robust features and fisher vector encoding. Signal Process. Lett. 24(2), 141–145 (2016)
Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., Hadid, A.: OULU-NPU: a mobile face presentation attack database with real-world variations. In: FG. IEEE (2017)
Boylan, J.F.: Will deep-fake technology destroy democracy? In: The New York Times (2018)
Bulat, A., Tzimiropoulos, G.: How far are we from solving the 2D & 3D face alignment problem? (and a dataset of 230,000 3D facial landmarks). In: ICCV. IEEE (2017)
Chang, H., Lu, J., Yu, F., Finkelstein, A.: Paired cycleGAN: asymmetric style transfer for applying and removing makeup. In: CVPR, IEEE (2018)
Chen, C., Xiong, Z., Liu, X., Wu, F.: Camera trace erasing. In: CVPR (2020)
Dale, K., Sunkavalli, K., Johnson, M.K., Vlasic, D., Matusik, W., Pfister, H.: Video face replacement. In: TOG. ACM (2011)
Deb, D., Zhang, J., Jain, A.K.: Advfaces: adversarial face synthesis. arXiv preprint arXiv:1908.05008 (2019)
Esser, P., Sutter, E., Ommer, B.: A variational U-Net for conditional appearance and shape generation. In: CVPR. IEEE (2018)
Feng, L., et al.: Integration of image quality and motion cues for face anti-spoofing: a neural network approach. J. Visual Commun. Image Represent. 38(2016), 451–460 (2016)
de Freitas Pereira, T., Anjos, A., De Martino, José M., Marcel, S.: LBP- TOP based countermeasure against face spoofing attacks. In: Park, J.-I. Kim, J. (eds.) ACCV 2012. LNCS, vol. 7728, pp. 121-132. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37410-4_11
de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: Can face anti-spoofing countermeasures work in a real world scenario? In: ICB. IEEE (2013)
Frischholz, R.W., Werner, A.: Avoiding replay-attacks in a face recognition system using head-pose estimation. In: International SOI Conference. IEEE (2003)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)
Guo, J., Zhu, X., Xiao, J., Lei, Z., Wan, G., Li, S.Z.: Improving face anti-spoofing by 3D virtual synthesis. arXiv preprint arXiv:1901.00488 (2019)
ISO/IEC JTC 1/SC 37 Biometrics: Information technology biometric presentation attack detection part 1: Framework. International organization for standardization. https://www.iso.org/obp/ui/iso (2016)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR. IEEE (2017)
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
Kollreider, K., Fronthaler, H., Faraj, M.I., Bigun, J.: Real-time face detection and motion analysis with application in “liveness” assessment. TIFS 2(3), 548–558 (2007)
Komulainen, J., Hadid, A., Pietikäinen, M.: Context based face anti-spoofing. In: BTAS. IEEE (2013)
Li, L., Feng, X., Boulkenafet, Z., Xia, Z., Li, M., Hadid, A.: An original face anti-spoofing approach using partial convolutional neural network. In: Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE (2016)
Liu, F., Zeng, D., Zhao, Q., Liu, X.: Disentangling features in 3D face shapes for joint face reconstruction and recognition. In: CVPR. IEEE (2018)
Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: CVPR. IEEE (2018)
Liu, Y., Jourabloo, A., Ren, W., Liu, X.: Dense face alignment. In: ICCV Workshops. IEEE (2017)
Liu, Y., Stehouwer, J., Jourabloo, A., Liu, X.: Deep tree learning for zero-shot face anti-spoofing. In: CVPR. IEEE (2019)
Liu, Y., Stehouwer, J., Jourabloo, A., Atoum, Y., Liu, X.: Presentation attack detection for face in mobile phones. In: Rattani, A., Derakhshani, R., Ross, A. (eds.) Selfie Biometrics. ACVPR, pp. 171–196. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26972-2_8
Määttä, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using micro-texture analysis. In: IJCB. IEEE (2011)
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: ICCV. IEEE (2017)
Pan, G., Sun, L., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: ICCV. IEEE (2007)
Patel, K., Han, H., Jain, A.K.: Cross-database face antispoofing with robust feature representation. In: You, Z., et al. (eds.) CCBR 2016. LNCS, vol. 9967, pp. 611–619. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46654-5_67
Patel, K., Han, H., Jain, A.K.: Secure face unlock: spoof detection on smartphones. TIFS 11(10), 2268–2283 (2016)
Qin, Y., et al.: Learning meta model for zero-and few-shot face anti-spoofing. arXiv preprint arXiv:1904.12490 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer (2015)
Schuckers, S.A.: Spoofing and anti-spoofing measures. Information Security technical report (2002)
Shao, R., Lan, X., Li, J., Yuen, P.C.: Multi-adversarial discriminative deep domain generalization for face presentation attack detection. In: CVPR. IEEE (2019)
Shao, R., Lan, X., Yuen, P.C.: Regularized fine-grained meta face anti-spoofing. arXiv preprint arXiv:1911.10771 (2019)
Stehouwer, J., Jourabloo, A., Liu, Y., Liu, X.: Noise modeling, synthesis and classification for generic object anti-spoofing. In: CVPR. IEEE (2020)
Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., Nießner, M.: Face2face: Real-time face capture and reenactment of RGB videos. In: CVPR. IEEE (2016)
Tran, L., Yin, X., Liu, X.: Disentangled representation learning GAN for pose-invariant face recognition. In: CVPR. IEEE (2017)
Tran, L., Yin, X., Liu, X.: Representation learning by rotating your faces. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 3007–3021 (2019)
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: CVPR. IEEE (2018)
Yang, J., Lei, Z., Li, S.Z.: Learn convolutional neural network for face anti-spoofing. arXiv preprint arXiv:1408.5601 (2014)
Yang, J., Lei, Z., Liao, S., Li, S.Z.: Face liveness detection with component dependent descriptor. In: ICB. IEEE (2013)
Yang, X., et al.: Face anti-spoofing: Model matters, so does data. In: CVPR. IEEE (2019)
Zakharov, E., Shysheya, A., Burkov, E., Lempitsky, V.: Few-shot adversarial learning of realistic neural talking head models. arXiv preprint arXiv:1905.08233 (2019)
Zhang, Z., et al.: Gait recognition via disentangled representation learning. In: CVPR. IEEE (2019)
Zhao, C., Qin, Y., Wang, Z., Fu, T., Shi, H.: Meta anti-spoofing: Learning to learn in face anti-spoofing. arXiv preprint arXiv:1904.12490 (2019)
Zollhöfer, M., et al.: State of the art on monocular 3D face reconstruction, tracking, and applications. Comput. Graph. Forum 37(2), 523–550 (2018)
Acknowledgment
This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2017-17020200004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, Y., Stehouwer, J., Liu, X. (2020). On Disentangling Spoof Trace for Generic Face Anti-spoofing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12363. Springer, Cham. https://doi.org/10.1007/978-3-030-58523-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-58523-5_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58522-8
Online ISBN: 978-3-030-58523-5
eBook Packages: Computer ScienceComputer Science (R0)