Abstract
Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border checks. While creating a morphed face detector (MFD), training on all possible attack types is essential to achieve good detection performance. Therefore, investigating new methods of creating morphing attacks drives the generalizability of MADs. Creating morphing attacks was performed on the image level, by landmark interpolation, or on the latent-space level, by manipulating latent vectors in a generative adversarial network. The earlier results in varying blending artifacts and the latter results in synthetic-like striping artifacts. This work presents the novel morphing pipeline, ReGenMorph, to eliminate the LMA blending artifacts by using a GAN-based generation, as well as, eliminate the manipulation in the latent space, resulting in visibly realistic morphed images compared to previous works. The generated ReGenMorph appearance is compared to recent morphing approaches and evaluated for face recognition vulnerability and attack detectability, whether as known or unknown attacks.
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
Similar content being viewed by others
References
Bojanowski, P., Joulin, A., Lopez-Paz, D., Szlam, A.: Optimizing the latent space of generative networks. In: ICML. Proceedings of Machine Learning Research, vol. 80, pp. 599–608. PMLR (2018)
Bolle, R., Pankanti, S.: Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society. Kluwer Academic Publishers, Norwell (1998)
Byrd, R., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16, 1190–1208 (1995). https://doi.org/10.1137/0916069
Damer, N., et al.: Detecting face morphing attacks by analyzing the directed distances of facial landmarks shifts. In: Brox, T., Bruhn, A., Fritz, M. (eds.) GCPR 2018. LNCS, vol. 11269, pp. 518–534. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12939-2_36
Damer, N., Boutros, F., Saladie, A.M., Kirchbuchner, F., Kuijper, A.: Realistic dreams: cascaded enhancement of GAN-generated images with an example in face morphing attacks. In: BTAS, pp. 1–10. IEEE (2019)
Damer, N., Grebe, J.H., Zienert, S., Kirchbuchner, F., Kuijper, A.: On the generalization of detecting face morphing attacks as anomalies: novelty vs. outlier detection. In: BTAS, pp. 1–5. IEEE (2019)
Damer, N., Saladie, A.M., Braun, A., Kuijper, A.: MorGAN: recognition vulnerability and attack detectability of face morphing attacks created by generative adversarial network. In: BTAS, pp. 1–10. IEEE (2018)
Damer, N., et al.: To detect or not to detect: the right faces to morph. In: ICB, pp. 1–8. IEEE (2019)
Damer, N., Zienert, S., Wainakh, Y., Saladie, A.M., Kirchbuchner, F., Kuijper, A.: A multi-detector solution towards an accurate and generalized detection of face morphing attacks. In: FUSION, pp. 1–8. IEEE (2019)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: CVPR, pp. 4690–4699. Computer Vision Foundation/IEEE (2019)
Ferrara, M., Franco, A., Maltoni, D.: The magic passport. In: IJCB, pp. 1–7. IEEE (2014)
Ferrara, M., Franco, A., Maltoni, D.: Face demorphing. IEEE Trans. Inf. Forensics Secur. 13(4), 1008–1017 (2018)
Fu, B., Spiller, N., Chen, C., Damer, N.: The effect of face morphing on face image quality. In: BIOSIG. LNI, Gesellschaft für Informatik e.V. (2021)
GmbH, C.S.: Facevacs technology - version 9.4.2 (2020). https://www.cognitec.com/facevacs-technology.html
Patrick, G., Mei, N., Kayee, H.: Ongoing face recognition vendor test (FRVT). NIST Interagency Report (2020)
Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87–102. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_6
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778. IEEE Computer Society (2016)
International Civil Aviation Organization, ICAO: Machine readable passports - part 9 - deployment of biometric identification and electronic storage of data in eMRTDs. Civil Aviation Organization (ICAO) (2015)
International Organization for Standardization: ISO/IEC DIS 30107-3:2016: Information Technology - Biometric presentation attack detection - P. 3: Testing and reporting (2017)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: ICLR. OpenReview.net (2018)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR, pp. 4401–4410. Computer Vision Foundation/IEEE (2019)
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: deep hypersphere embedding for face recognition. In: CVPR, pp. 6738–6746. IEEE Computer Society (2017)
Markets and Markets: Facial Recognition Market by Component (Software Tools and Services), Technology, Use Case (Emotion Recognition, Attendance Tracking and Monitoring, Access Control, Law Enforcement), End-User, and Region - Global Forecast to 2022. Report, November 2017
Massoli, F.V., Carrara, F., Amato, G., Falchi, F.: Detection of face recognition adversarial attacks. Comput. Vis. Image Underst. 202, 103103 (2021)
NIST: FRVT morph web site. NIST Interagency Report (2020)
Phillips, P.J., et al.: Overview of the face recognition grand challenge. In: CVPR (1), pp. 947–954. IEEE Computer Society (2005)
Qin, L., Peng, F., Venkatesh, S., Ramachandra, R., Long, M., Busch, C.: Low visual distortion and robust morphing attacks based on partial face image manipulation. IEEE Trans. Biom. Behav. Identity Sci. 3(1), 72–88 (2021)
Raghavendra, R., Raja, K.B., Venkatesh, S., Busch, C.: Face morphing versus face averaging: vulnerability and detection. In: IJCB, pp. 555–563. IEEE (2017)
Ramachandra, R., Venkatesh, S., Raja, K.B., Busch, C.: Towards making morphing attack detection robust using hybrid scale-space colour texture features. In: ISBA, pp. 1–8. IEEE (2019)
Scherhag, U., Kunze, J., Rathgeb, C., Busch, C.: Face morph detection for unknown morphing algorithms and image sources: a multi-scale block local binary pattern fusion approach. IET Biom. 9(6), 278–289 (2020)
Scherhag, U., et al.: Biometric systems under morphing attacks: assessment of morphing techniques and vulnerability reporting. In: BIOSIG. LNI, vol. P-270, pp. 149–159. GI/IEEE (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Venkatesh, S., Ramachandra, R., Raja, K.B., Busch, C.: Single image face morphing attack detection using ensemble of features. In: FUSION, pp. 1–6. IEEE (2020)
Venkatesh, S., Zhang, H., Ramachandra, R., Raja, K.B., Damer, N., Busch, C.: Can GAN generated morphs threaten face recognition systems equally as landmark based morphs? - vulnerability and detection. In: IWBF, pp. 1–6. IEEE (2020)
Zhang, H., Venkatesh, S., Ramachandra, R., Raja, K.B., Damer, N., Busch, C.: MIPGAN - generating robust and high quality morph attacks using identity prior driven GAN. CoRR abs/2009.01729 (2020)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)
Acknowledgment
This research work has been funded by the German Federal Ministry of Education and Research and the Hessian Ministry of Higher Education, Research, Science and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Damer, N. et al. (2021). ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13017. Springer, Cham. https://doi.org/10.1007/978-3-030-90439-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-90439-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90438-8
Online ISBN: 978-3-030-90439-5
eBook Packages: Computer ScienceComputer Science (R0)