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Volume 20 Issue 6
December  2023
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Mahdi Ghafourian, Julian Fierrez, Ruben Vera-Rodriguez, Aythami Morales, Ignacio Serna. OTB-morph: One-time Biometrics via Morphing. Machine Intelligence Research, vol. 20, no. 6, pp.855-871, 2023. https://doi.org/10.1007/s11633-023-1432-x
Citation: Mahdi Ghafourian, Julian Fierrez, Ruben Vera-Rodriguez, Aythami Morales, Ignacio Serna. OTB-morph: One-time Biometrics via Morphing. Machine Intelligence Research, vol. 20, no. 6, pp.855-871, 2023. https://doi.org/10.1007/s11633-023-1432-x
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OTB-morph: One-time Biometrics via Morphing

doi: 10.1007/s11633-023-1432-x

  • Biometrics and Data Pattern Analytics Laboratory (BiDA Lab), Universidad Autonoma de Madrid, Madrid 28049, Spain

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  • Author Bio:

    Mahdi Ghafourian received the B. Sc. degree in computer science from the Islamic Azad University of Mashhad, Iran in 2011, and the M. Sc. degree in information security and assurance from the Imam Reza University, Iran in 2016. He achieved the second position among top Master′s degree graduates. In 2021, he started the Ph.D. with Marie Curie scholarship within the EU ITN project PriMa (Privacy Matters) in the Biometrics and Data Pattern Analytics Laboratory – BiDA-Lab, at the Universidad Autonoma de Madrid, Spain.His research interests include information security, biometrics protection, face recognition, adversarial examples and federated learning. E-mail: mahdi.ghafourian@uam.es (Corresponding author) ORCID iD: 0000-0003-4206-4873

    Julian Fierrez received the M. Sc. and the Ph. D. degrees in telecommunications engineering from Universidad Politecnica de Madrid, Spain in 2001 and 2006, respectively. Since 2004, he is at Universidad Autonoma de Madrid, Spain where he is associate professor since 2010. He is Associate Editor for Information Fusion, IEEE Transactions on Information Forensics and Security, and IEEE Transactions on Image Processing. He has received best papers awards at AVBPA, ICB, IJCB, ICPR, ICPRS, and Pattern Recognition Letters; and several research distinctions, including: EBF European Biometric Industry Award 2006, EURASIP Best Ph. D. Award 2012, Miguel Catalan Award to the Best Researcher under 40 in the Community of Madrid in the general area of Science and Technology, and the IAPR Young Biometrics Investigator Award 2017. Since 2020, he is member of the ELLIS Society. His research interests include signal and image processing, AI fundamentals and applications, HCI, forensics, and biometrics for security and human behavior analysis. E-mail: julian.fierrez@uam.es

    Ruben Vera-Rodriguez received the M. Sc. degree in telecommunications engineering from Universidad de Sevilla Spain, Spain in 2006, and the Ph. D. degree in electrical and electronic engineering from Swansea University, UK in 2010. Since 2010, he has been affiliated with the Biometric Recognition Group, Universidad Autonoma de Madrid, Spain, where he is currently an associate professor since 2018. Ruben has published over 100 scientific articles published in international journals and conferences. He is actively involved in several National and European projects focused on biometrics. He has been Program Chair for the 51st IEEE International Carnahan Conference on Security and Technology (ICCST) in 2017; the 23rd Iberoamerican Congress on Pattern Recognition (CIARP 2018) in 2018; and the International Conference on Biometric Engineering and Applications (ICBEA 2019) in 2019. His research interests include signal and image processing, pattern recognition, HCI, and biometrics, with emphasis on signature, face, gait verification and forensic applications of biometrics. E-mail: ruben.vera@uam.es

    Aythami Morales received the M. Sc. degree in electrical engineering from Universidad de Las Palmas de Gran Canaria, Spain in 2006, the Ph. D. degree in artificial intelligence from La Universidad de Las Palmas de Gran Canaria, Spain in 2011. Since 2017, he is an associate professor with the Universidad Autonoma de Madrid, Spain. His interests include machine learning, biometric processing, security and privacy. E-mail: aythami.morales@uam.es

    Ignacio Serna received the B. Sc. degree in mathematics and the B. Sc. degree in computer science from the Autonomous University of Madrid, Spain in 2018, and the M. Sc. degree in artificial intelligence from the Autonomous University of Madrid, Spain in 2020. He is currently a Ph. D. degree candidate in computer science at the BiDA-Lab, Spain. His research interests include computer vision, pattern recognition and explainable AI, with applications to biometric. E-mail: ignacio.serna@uam.es

  • Received Date: 2022-09-27
  • Accepted Date: 2023-03-02
  • Publish Online: 2023-06-01
  • Publish Date: 2023-12-01
    Abstract
  • Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization attacks. Our proposed scheme is based on time-varying keys (random biometrics in our case) and morphing transformations. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.

     

    • 41 https://www.kaggle.com/datasets/prasoonkottarathil/face-mask-lite-dataset2 Faces in Figs. 4 and 5 are selected from LFW publicly available at https://www.kaggle.com/datasets/jessicali9530/lfw-dataset
    13 This kind of impostors are different from the attackers considered in Section 3, who have much more information to attack the system compared to a random impostor that just tries to illegally access the system by using his own face input and no other methods to improve the attack success.
    24 https://github.com/peteryuX/arcface-tf25 https://github.com/rcmalli/keras-vggface
    3https://github.com/rcmalli/keras-vggface
    56 https://github.com/leondgarse/Keras_insightface
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    • References(55)
  • [1]
    J. Fierrez, A. Morales, J. Ortega-Garcia. Biometrics security. Encyclopedia of Cryptography, Security and Privacy, S. Jajodia, P. Samarati, M. Yung, Eds., Berlin, Heidelberg, Germany: Springer, pp. 1–3, 2021. DOI: 10.1007/978-3-642-27739-9_1603-1.
    [2]
    G. Jaswal, V. Kanhangad, R. Ramachandra. AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges, Boca Raton, USA: CRC Press, 2021.
    [3]
    M. R. Freire, J. Fierrez, J. Galbally, J. Ortega-Garcia. Biometric hashing based on genetic selection and its application to on-line signatures. In Proceedings of International Conference on Biometrics, Springer, Seoul, Republic of Korea, pp. 1134–1143, 2007. DOI: 10.1007/978-3-540-74549-5_118.
    [4]
    M. Gomez-Barrero, J. Galbally, A. Morales, J. Fierrez. Privacy-preserving comparison of variable-length data with application to biometric template protection. IEEE Access, vol. 5, pp. 8606–8619, 2017. DOI: 10.1109/ACCESS.2017.2691578.
    [5]
    K. Nandakumar, A. K. Jain. Biometric template protection: Bridging the performance gap between theory and practice. IEEE Signal Processing Magazine, vol. 32, no. 5, pp. 88–100, 2015. DOI: 10.1109/MSP.2015.2427849.
    [6]
    M. Gomez-Barrero, E. Maiorana, J. Galbally, P. Campisi, J. Fierrez. Multi-biometric template protection based on homomorphic encryption. Pattern Recognition, vol. 67, pp. 149–163, 2017. DOI: 10.1016/j.patcog.2017.01.024.
    [7]
    Q. Li, Z. N. Sun, R. He, T. N. Tan. Deep supervised discrete hashing. In Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, pp. 2479–2488, 2017.
    [8]
    M. Ghafourian, B. Sumer, R. Vera-Rodriguez, J. Fierrez, R. Tolosana, A. Moralez, E. Kindt. Combining blockchain and biometrics: A survey on technical aspects and a first legal analysis, [Online], Available: https://arxiv.org/abs/2302.10883, 2023.
    [9]
    V. M. Patel, N. K. Ratha, R. Chellappa. Cancelable biometrics: A review. IEEE Signal Processing Magazine, vol. 32, no. 5, pp. 54–65, 2015. DOI: 10.1109/MSP.2015.2434151.
    [10]
    Manisha, N. Kumar. Cancelable biometrics: A comprehensive survey. Artificial Intelligence Review, vol. 53, no. 5, pp. 3403–3446, 2020. DOI: 10.1007/s10462-019-09767-8.
    [11]
    S. M. Bellovin. Frank miller: Inventor of the one-time pad. Cryptologia, vol. 35, no. 3, pp. 203–222, 2011. DOI: 10.1080/01611194.2011.583711.
    [12]
    J. C. Neves, R. Tolosana, R. Vera-Rodriguez, V. Lopes, H. Proença, J. Fierrez. GANprintR: Improved fakes and evaluation of the state of the art in face manipulation detection. IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 5, pp. 1038–1048, 2020. DOI: 10.1109/JSTSP.2020.3007250.
    [13]
    U. Scherhag, C. Rathgeb, J. Merkle, R. Breithaupt, C. Busch. Face recognition systems under morphing attacks: A survey. IEEE Access, vol. 7, pp. 23012–23026, 2019. DOI: 10.1109/ACCESS.2019.2899367.
    [14]
    M. Ghafourian, J. Fierrez, R. Vera-Rodriguez, I. Serna, A. Morales. OTB-morph: One-time biometrics via morphing applied to face templates. In Proceedings of IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, IEEE, Waikoloa, USA, pp. 321–329, 2022. DOI: 10.1109/WACVW54805.2022.00038.
    [15]
    N. K. Ratha, J. H. Connell, R. M. Bolle. Enhancing security and privacy in biometrics-based authentication systems. IBM Systems Journal, vol. 40, no. 3, pp. 614–634, 2001. DOI: 10.1147/sj.403.0614.
    [16]
    A. T. B. Jin, D. N. C. Ling, A. Goh. BioHashing: Two factor authentication featuring fingerprint data and tokenised random number. Pattern Recognition, vol. 37, no. 11, pp. 2245–2255, 2004. DOI: 10.1016/j.patcog.2004.04.011.
    [17]
    R. Ang, R. Safavi-Naini, L. McAven. Cancelable key-based fingerprint templates. In Proceedings of the 10th Australasian Conference on Information Security and Privacy, Springer, Brisbane, Australia, pp. 242–252, 2005. DOI: 10.1007/11506157_21.
    [18]
    C. Lee, J. Y. Choi, K. A. Toh, S. Lee, J. Kim. Alignment-free cancelable fingerprint templates based on local minutiae information. IEEE Transactions on Systems,Man,and Cybernetics – Part B (Cybernetics), vol. 37, no. 4, pp. 980–992, 2007. DOI: 10.1109/TSMCB.2007.896999.
    [19]
    N. K. Ratha, S. Chikkerur, J. H. Connell, R. M. Bolle. Generating cancelable fingerprint templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 561–572, 2007. DOI: 10.1109/TPAMI.2007.1004.
    [20]
    F. Quan, S. Fei, C. Anni, Z. Feifei. Cracking cancelable fingerprint template of Ratha. In Proceedings of International Symposium on Computer Science and Computational Technology, IEEE, Shanghai, China, pp. 572–575, 2008. DOI: 10.1109/ISCSCT.2008.226.
    [21]
    E. Maiorana, P. Campisi, J. Fierrez, J. Ortega-Garcia, A. Neri. Cancelable templates for sequence-based biometrics with application to on-line signature recognition. IEEE Transactions on Systems,Man,and Cybernetics – Part A:Systems and Humans, vol. 40, no. 3, pp. 525–538, 2010. DOI: 10.1109/TSMCA.2010.2041653.
    [22]
    O. Ouda, N. Tsumura, T. Nakaguchi. Tokenless cancelable biometrics scheme for protecting iris codes. In Proceedings of the 20th International Conference on Pattern Recognition, IEEE, Istanbul, Turkey, pp. 882–885, 2010. DOI: 10.1109/ICPR.2010.222.
    [23]
    J. K. Pillai, V. M. Patel, R. Chellappa, N. K. Ratha. Sectored random projections for cancelable iris biometrics. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, USA, pp. 1838–1841, 2010. DOI: 10.1109/ICASSP.2010.5495383.
    [24]
    M. Ferrara, D. Maltoni, R. Cappelli. Noninvertible minutia cylinder-code representation. IEEE Transactions on Information Forensics and Security, vol. 7, no. 6, pp. 1727–1737, 2012. DOI: 10.1109/TIFS.2012.2215326.
    [25]
    M. Gomez-Barrero, C. Rathgeb, J. Galbally, C. Busch, J. Fierrez. Unlinkable and irreversible biometric template protection based on Bloom filters. Information Sciences, vol. 370–371, pp. 18–32, 2016. DOI: 10.1016/j.ins.2016.06.046.
    [26]
    M. Gomez-Barrero, C. Rathgeb, J. Galbally, J. Fierrez, C. Busch. Protected facial biometric templates based on local Gabor patterns and adaptive bloom filters. In Proceedings of the 22nd International Conference on Pattern Recognition, IEEE, Stockholm, Sweden, pp. 4483–4488, 2014. DOI: 10.1109/ICPR.2014.767.
    [27]
    C. Rathgeb, M. Gomez-Barrero, C. Busch, J. Galbally, J. Fierrez. Towards cancelable multi-biometrics based on bloom filters: A case study on feature level fusion of face and iris. In Proceedings of the 3rd International Workshop on Biometrics and Forensics, IEEE, Gjovik, Norway, 2015. DOI: 10.1109/IWBF.2015.7110225.
    [28]
    Y. J. Chin, T. S. Ong, A. B. J. Teoh, K. O. M. Goh. Integrated biometrics template protection technique based on fingerprint and palmprint feature-level fusion. Information Fusion, vol. 18, pp. 161–174, 2014. DOI: 10.1016/j.inffus.2013.09.001.
    [29]
    Y. L. Lai, Z. Jin, A. B. J. Teoh, B. M. Goi, W. S. Yap, T. Y. Chai, C. Rathgeb. Cancellable iris template generation based on indexing-first-one hashing. Pattern Recognition, vol. 64, pp. 105–117, 2017. DOI: 10.1016/j.patcog.2016.10.035.
    [30]
    D. Sadhya, B. Raman. Generation of cancelable iris templates via randomized bit sampling. IEEE Transactions on Information Forensics and, vol. 14, no. 11, pp. 2972–2986, 2019. DOI: 10.1109/TIFS.2019.2907014.
    [31]
    S. Kirchgasser, C. Kauba, A. Uhl. Cancellable biometrics for finger vein recognition-application in the feature domain. Handbook of Vascular Biometrics, A. Uhl, C. Busch, S. Marcel, R. Veldhuis, Eds., Cham, Germany: Springer, pp. 481–506, 2020. DOI: 10.1007/978-3-030-27731-4_16.
    [32]
    S. Kirchgasser, A. Uhl, Y. Martinez-Diaz, H. Mendez-Vazquez. Is warping-based cancellable biometrics (still) sensible for face recognition? In Proceedings of IEEE International Joint Conference on Biometrics, Houston, USA, pp. 1–9, 2020. DOI: 10.1109/IJCB48548.2020.9304870.
    [33]
    I. S. Badr, A. G. Radwan, E. S. M. El-Rabaie, L. A. Said, G. M. El Banby, W. El-Shafai, F. E. A. El-Samie. Cancellable face recognition based on fractional-order Lorenz chaotic system and HAAR wavelet fusion. Digital Signal Processing, vol. 116, Article number 103103, 2021. DOI: 10.1016/J.DSP.2021.103103.
    [34]
    X. B. Dong, S. Cho, Y. Kim, S. Kim, A. B. J. Teoh. Deep rank hashing network for cancellable face identification. Pattern Recognition, vol. 131, Article number 108886, 2022. DOI: 10.1016/J.PATCOG.2022.108886.
    [35]
    D. Chang, S. Garg, M. Hasan, S. Mishra. Cancelable multi-biometric approach using fuzzy extractor and novel bit-wise encryption. IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3152–3167, 2020. DOI: 10.1109/TIFS.2020.2983250.
    [36]
    J. Galbally. A new Foe in biometrics: A narrative review of side-channel attacks. Computers &Security, vol. 96, Article number 101902, 2020. DOI: 10.1016/j.cose.2020.101902.
    [37]
    J. Galbally, S. Carballo, J. Fierrez, J. Ortega-Garcia. Vulnerability assessment of fingerprint matching based on time analysis. In Proceedings of the European Workshop on Biometrics and Identity Management, Springer, Madrid, Spain, pp. 285–292, 2009. DOI: 10.1007/978-3-642-04391-8_37.
    [38]
    L. G. Zhu, S. Han. Deep leakage from gradients. In Federated Learning, Q. Yang, L. X. Fan, H. Yu, Eds., Cham, Germany: Springer, pp. 17–31, 2020. DOI: 10.1007/978-3-030-63076-8_2.
    [39]
    J. Galbally, C. McCool, J. Fierrez, S. Marcel, J. Ortega-Garcia. On the vulnerability of face verification systems to hill-climbing attacks. Pattern Recognition, vol. 43, no. 3, pp. 1027–1038, 2010. DOI: 10.1016/j.patcog.2009.08.022.
    [40]
    M. Gomez-Barrero, J. Galbally, J. Fierrez. Efficient software attack to multimodal biometric systems and its application to face and iris fusion. Pattern Recognition Letters, vol. 36, pp. 243–253, 2014. DOI: 10.1016/j.patrec.2013.04.029.
    [41]
    M. Gomez-Barrero, J. Galbally, J. Fierrez, J. Ortega-Garcia. Face verification put to test: A hill-climbing attack based on the uphill-simplex algorithm. In Proceedings of the 5th IAPR International Conference on Biometrics, IEEE, New Delhi, India, pp. 40–45, 2012. DOI: 10.1109/ICB.2012.6199756.
    [42]
    M. Ghafoorian, D. Abbasinezhad-Mood, H. Shakeri. A thorough trust and reputation based RBAC model for secure data storage in the cloud. IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 4, pp. 778–788, 2019. DOI: 10.1109/TPDS.2018.2870652.
    [43]
    M. Ghafoorian, M. Nikooghadam. An anonymous and secure key agreement protocol for NFC applications using pseudonym. Wireless Networks, vol. 26, no. 6, pp. 4269–4285, 2020. DOI: 10.1007/s11276-020-02319-x.
    [44]
    R. Tolosana, P. Delgado-Santos, A. Perez-Uribe, R. Vera-Rodriguez, J. Fierrez, A. Morales. DeepWriteSYN: On-line handwriting synthesis via deep short-term representations. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, Palo Alto, USA, pp. 600–608, 2021. DOI: 10.1609/aaai.v35i1.16139.
    [45]
    Q. Cao, L. Shen, W. D. Xie, O. M. Parkhi, A. Zisserman. VGGFace2: A dataset for recognising faces across pose and age. In Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition, Xi′an, China, pp. 67–74, 2018. DOI: 10.1109/FG.2018.00020.
    [46]
    D. Yi, Z. Lei, S. C. Liao, S. Z. Li. Learning face representation from scratch, [Online], Available: https://arxiv.org/abs/1411.7923, 2014.
    [47]
    E. Gonzalez-Sosa, J. Fierrez, R. Vera-Rodriguez, F. Alonso-Fernandez. Facial soft biometrics for recognition in the wild: Recent works, annotation, and COTS evaluation. IEEE Transactions on Information Forensics and Security, vol. 13, no. 8, pp. 2001–2014, 2018. DOI: 10.1109/TIFS.2018.2807791.
    [48]
    G. B. Huang, M. Mattar, T. Berg, E. Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. In Proceedings of Workshop on Faces in “Real-Life” Images: Detection, Alignment, and Recognition, Marseille, France, 2008.
    [49]
    K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 770–778, 2016. DOI: 10.1109/CVPR.2016.90.
    [50]
    J. K. Deng, J. Guo, N. N. Xue, S. Zafeiriou. ArcFace: Additive angular margin loss for deep face recognition. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Long Beach, USA, pp. 4685–4694, 2019. DOI: 10.1109/CVPR.2019.00482.
    [51]
    M. Kim, A. K. Jain, X. M. Liu. AdaFace: Quality adaptive margin for face recognition. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, New Orleans, USA, pp. 18729–18738, 2022. DOI: 10.1109/CVPR52688.2022.01819.
    [52]
    H. W. Zhang, Q. Li, Z. N. Sun, Y. F. Liu. Combining data-driven and model-driven methods for robust facial landmark detection. IEEE Transactions on Information Forensics and Security, vol. 13, no. 10, pp. 2409–2422, 2018. DOI: 10.1109/TIFS.2018.2800901.
    [53]
    U. Scherhag, D. Fischer, S. Isadskiy, J. Otte, C. Busch. Morphing attack detection using laplace operator based features. Norsk IKT-Konferanse for Forskning Og Utdanning, no. 3, 2020.
    [54]
    S. Venkatesh, R. Ramachandra, K. Raja, C. Busch. Face morphing attack generation and detection: A comprehensive survey. IEEE Transactions on Technology and Society, vol. 2, no. 3, pp. 128–145, 2021. DOI: 10.1109/TTS.2021.3066254.
    [55]
    M. Ghafourian, J. Fierrez, L. F. Gomez, R. Vera-Rodriguez, A. Morales, Z. Rezgui, R. Veldhuis. Toward face biometric de-identification using adversarial examples, [Online], Available: https://arxiv.org/abs/2302.03657, 2023.
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