Abstract
A face morphing attack image can be verified to multiple identities, making this attack a major vulnerability to processes based on identity verification, such as border checks. Various methods have been proposed to detect face morphing attacks, however, with low generalizability to unexpected post-morphing processes. A major post-morphing process is the print and scan operation performed in many countries when issuing a passport or identity document. In this work, we address this generalization problem by adapting a pixel-wise supervision approach where we train a network to classify each pixel of the image into an attack or not, rather than only having one label for the whole image. Our pixel-wise morphing attack detection (PW-MAD) solution proved to perform more accurately than a set of established baselines. More importantly, PW-MAD shows high generalizability in comparison to related works, when evaluated on unknown re-digitized attacks. Additionally to our PW-MAD approach, we create a new face morphing attack dataset with digital and re-digitized samples, namely the LMA-DRD dataset that is publicly available for research purposes upon request.
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
Aghdaie, P., Chaudhary, B., Soleymani, S., Dawson, J.M., Nasrabadi, N.M.: Detection of morphed face images using discriminative wavelet sub-bands. In: IWBF, pp. 1–6. IEEE (2021)
Amos, B., Ludwiczuk, B., Satyanarayanan, M.: Openface: a general-purpose face recognition library with mobile applications. Technical report, CMU-CS-16-118, CMU School of Computer Science (2016)
Bolle, R., Pankanti, S.: Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society. Kluwer Academic Publishers, Norwell (1998)
Boutros, F., Damer, N., Kirchbuchner, F., Kuijper, A.: Elasticface: elastic margin loss for deep face recognition (2021)
Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: Vggface2: a dataset for recognising faces across pose and age. In: FG, pp. 67–74. IEEE CS (2018)
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., Dimitrov, K.: Practical view on face presentation attack detection. In: BMVC. BMVA Press (2016)
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., et al.: Regenmorph: visibly realistic GAN generated face morphing attacks by attack re-generation. CoRR abs/2108.09130 (2021)
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., et al.: Crazyfaces: unassisted circumvention of watchlist face identification. In: BTAS, pp. 1–9. IEEE (2018)
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)
Debiasi, L., et al.: On the detection of GAN-based face morphs using established morph detectors. In: Ricci, E., Rota Bulò, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019. LNCS, vol. 11752, pp. 345–356. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30645-8_32
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248–255. IEEE Computer Society (2009)
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)
Fang, M., Damer, N., Boutros, F., Kirchbuchner, F., Kuijper, A.: Iris presentation attack detection by attention-based and deep pixel-wise binary supervision network. In: IJCB, pp. 1–8. IEEE (2021)
Ferrara, M., Franco, A., Maltoni, D.: The magic passport. In: IJCB. IEEE (2014)
Ferrara, M., Franco, A., Maltoni, D.: Face morphing detection in the presence of printing/scanning and heterogeneous image sources. IET Biometrics 10(3), 290–303 (2021)
Frontex: Best practice technical guidelines for automated border control (ABC) systems (2015)
George, A., Marcel, S.: Deep pixel-wise binary supervision for face presentation attack detection. In: 2019 International Conference on Biometrics, ICB 2019, Crete, Greece, 4–7 June 2019, pp. 1–8. IEEE (2019)
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21–26 July 2017, pp. 2261–2269. IEEE Computer Society (2017)
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)
Khodabakhsh, A., Raghavendra, R., Raja, K.B., Wasnik, P.S., Busch, C.: Fake face detection methods: can they be generalized? In: BIOSIG. LNI, vol. P-282, pp. 1–6. GI/IEEE (2018)
Makrushin, A., Neubert, T., Dittmann, J.: Automatic generation and detection of visually faultless facial morphs. In: VISIGRAPP (6: VISAPP), pp. 39–50. SciTePress (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)
Raghavendra, R., Raja, K.B., Busch, C.: Detecting morphed face images. In: BTAS, pp. 1–7. IEEE (2016)
Raghavendra, R., Raja, K.B., Venkatesh, S., Busch, C.: Face morphing versus face averaging: vulnerability and detection. In: IJCB, pp. 555–563. IEEE (2017)
Raghavendra, R., Raja, K.B., Venkatesh, S., Busch, C.: Transferable deep-CNN features for detecting digital and print-scanned morphed face images. In: CVPR Workshops, pp. 1822–1830. IEEE Computer Society (2017)
Ramachandra, R., Venkatesh, S., Raja, K., Busch, C.: Detecting face morphing attacks with collaborative representation of steerable features. In: Chaudhuri, B.B., Nakagawa, M., Khanna, P., Kumar, S. (eds.) Proceedings of 3rd International Conference on Computer Vision and Image Processing. AISC, vol. 1022, pp. 255–265. Springer, Singapore (2020). https://doi.org/10.1007/978-981-32-9088-4_22
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., 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)
Scherhag, U., Raghavendra, R., Raja, K.B., Gomez-Barrero, M., Rathgeb, C., Busch, C.: On the vulnerability of face recognition systems towards morphed face attacks. In: IWBF, pp. 1–6. IEEE (2017)
Scherhag, U., Rathgeb, C., Busch, C.: Performance variation of morphed face image detection algorithms across different datasets. In: IWBF, pp. 1–6. IEEE (2018)
Scherhag, U., Rathgeb, C., Merkle, J., Busch, C.: Deep face representations for differential morphing attack detection. IEEE TIFS 15, 3625–3639 (2020)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Soleymani, S., Chaudhary, B., Dabouei, A., Dawson, J., Nasrabadi, N.M., et al.: Differential morphed face detection using deep siamese networks. In: Del Bimbo, A. (ed.) ICPR 2021. LNCS, vol. 12666, pp. 560–572. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68780-9_44
Spreeuwers, L.J., Schils, M., Veldhuis, R.N.J.: Towards robust evaluation of face morphing detection. In: EUSIPCO, pp. 1027–1031. IEEE (2018)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016, pp. 2818–2826. IEEE Computer Society (2016)
Venkatesh, S., Ramachandra, R., Raja, K., Busch, C.: Face morphing attack generation and detection: a comprehensive survey. IEEE Trans. Technol. Soc. 2(3), 128–145 (2021). https://doi.org/10.1109/TTS.2021.3066254
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 strong and high quality morphing attacks using identity prior driven GAN. IEEE Trans. Biom. Behav. Identity Sci. 3(3), 365–383 (2021)
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., Spiller, N., Fang, M., Boutros, F., Kirchbuchner, F., Kuijper, A. (2021). PW-MAD: Pixel-Wise Supervision for Generalized Face Morphing Attack Detection. 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_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-90439-5_23
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)