Hybrid method for active face anti-spoofing based on close-up challenge
Resumo
Facial authentication on mobile devices has become prevalent in various applications. Face Liveness Detection, or Face Anti-Spoofing (FAS), focuses on identifying attempts by malicious users to impersonate someone else or hide their own identity. One specific branch within this field is active liveness detection, which involves analyzing both the input signal and user behavior while they perform a required task to verify the authenticity of the presented face. Despite the significant amount of research in FAS, active liveness detection remains mostly underexplored. This gap has led to outdated methods, insufficient testing of proposed active techniques in diverse scenarios, and a lack of comparative analysis between different approaches. In this paper, we explore these differences by comparing the performance of the latest existing close-up methods with baseline models using ResNet-18 and ResNet-50. Furthermore, we introduce a new model that builds on previous work, combining projective invariants with facial embedding for robust feature extraction. This approach directly improves upon existing techniques, surpassing other baselines in detecting spoofing attempts.Referências
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M. Killioğlu, M. Taşkiran, and N. Kahraman, “Anti-spoofing in face recognition with liveness detection using pupil tracking,” in 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2017, pp. 000 087–000 092.
M. Singh and A. Arora, “A robust anti-spoofing technique for face liveness detection with morphological operations,” Optik, vol. 139, pp. 347–354, 2017.
M. Mohzary, K. J. Almalki, B.-Y. Choi, and S. Song, “Your eyes show what your eyes see (y-eyes): Challenge-response anti-spoofing method for mobile security using corneal specular reflections,” in 1st Workshop on Security and Privacy for Mobile AI, 2021, p. 25–30.
Y. Liu, Y. Tai, J. Li, S. Ding, C. Wang, F. Huang, D. Li, W. Qi, and R. Ji, “Aurora guard: Real-time face anti-spoofing via light reflection,” CoRR, vol. abs/1902.10311, 2019. [Online]. Available: [link]
J. M. D. Martino, Q. Qiu, T. Nagenalli, and G. Sapiro, “Liveness detection using implicit 3D features,” CoRR, vol. abs/1804.06702, 2018. [Online]. Available: [link]
W. Xu, J. Liu, S. Zhang, Y. Zheng, F. Lin, J. Han, F. Xiao, and K. Ren, “Rface: Anti-spoofing facial authentication using cots rfid,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1–10.
M. Ezz, M. Ayman, and A. Elshenawy Elsefy, “Challenge-response emotion authentication algorithm using modified horizontal deep learning,” Intelligent Automation and Soft Computing, vol. 35, pp. 3659–3675, 09 2022.
Z. Ming, J. Chazalon, M. M. Luqman, M. Visani, and J.-C. Burie, “Facelivenet: End-to-end networks combining face verification with interactive facial expression-based liveness detection,” in 2018 24th Int. Conf. on Pattern Recognition (ICPR). IEEE, 2018, pp. 3507–3512.
Y. Li, Z. Wang, Y. Li, R. Deng, B. Chen, W. Meng, and H. Li, “A closer look tells more: A facial distortion based liveness detection for face authentication,” in Asia CCS ’19: ACM Asia Conference on Computer and Communications Security, 07 2019, pp. 241–246.
A. Castelblanco, E. Rivera, J. Solano, L. Tengana, C. López, and M. Ochoa, “Dynamic face authentication systems: Deep learning verification for camera close-up and head rotation paradigms,” Comput. Secur., vol. 115, no. C, apr 2022.
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A. Anjos and S. Marcel, “Countermeasures to photo attacks in face recognition: A public database and a baseline,” in 2011 International Joint Conference on Biometrics (IJCB), 2011, pp. 1–7.
Z. Zhang, J. Yan, S. Liu, Z. Lei, D. Yi, and S. Z. Li, “A face antispoofing database with diverse attacks,” in 2012 5th IAPR Int. Conf. on Biometrics (ICB), 2012, pp. 26–31.
I. Chingovska, A. Anjos, and S. Marcel, “On the effectiveness of local binary patterns in face anti-spoofing,” in 2012 BIOSIG - Int. Conf. of Biometrics Special Interest Group (BIOSIG), 2012, pp. 1–7.
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K. Patel, H. Han, and A. K. Jain, “Secure face unlock: Spoof detection on smartphones,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 10, pp. 2268–2283, 2016.
A. Agarwal, D. Yadav, N. Kohli, R. Singh, M. Vatsa, and A. Noore, “Face presentation attack with latex masks in multispectral videos,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017.
Z. Boulkenafet, J. Komulainen, L. Li, X. Feng, and A. Hadid, “OULU-NPU: A mobile face presentation attack database with real-world variations,” in 2017 12th IEEE Int. Conf. on Automatic Face & Gesture Recognition (FG 2017), 2017, pp. 612–618.
Y. Liu, A. Jourabloo, and X. Liu, “Learning deep models for face anti-spoofing: Binary or auxiliary supervision,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
Y. Liu, J. Stehouwer, A. Jourabloo, and X. Liu, “Deep tree learning for zero-shot face anti-spoofing,” in 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4675–4684.
Z. Mostaani, A. George, G. Heusch, D. Geissbühler, and S. Marcel, “The high-quality wide multi-channel attack (HQ-WMCA) database,” CoRR, vol. abs/2009.09703, 2020.
Z. Wang, Z. Yu, C. Zhao, X. Zhu, Y. Qin, Q. Zhou, F. Zhou, and Z. Lei, “Deep spatial gradient and temporal depth learning for face anti-spoofing,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 5041–5050.
Y. Zhang, Z. Yin, Y. Li, G. Yin, J. Yan, J. Shao, and Z. Liu, “CelebA-spoof: Large-scale face anti-spoofing dataset with rich annotations,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16. Springer, 2020, pp. 70–85.
D. Wang, J. Guo, Q. Shao, H. He, Z. Chen, C. Xiao, A. Liu, S. Escalera, H. J. Escalante, Z. Lei et al., “Wild face anti-spoofing challenge 2023: Benchmark and results,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 6379–6390.
Z. Boulkenafet, J. Komulainen, and A. Hadid, “Face antispoofing using speeded-up robust features and fisher vector encoding,” IEEE Signal Processing Letters, vol. 24, no. 2, pp. 141–145, 2017.
T. de Freitas Pereira, A. Anjos, J. M. De Martino, and S. Marcel, “Lbp-top based countermeasure against face spoofing attacks,” in Computer Vision - ACCV 2012 Workshops, J.-I. Park and J. Kim, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 121–132.
J. Komulainen, A. Hadid, and M. Pietikäinen, “Context based face anti-spoofing,” in 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2013, pp. 1–8.
Z. Yu, C. Zhao, Z. Wang, Y. Qin, Z. Su, X. Li, F. Zhou, and G. Zhao, “Searching central difference convolutional networks for face anti-spoofing,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 5294–5304.
Z. Yu, Y. Qin, H. Zhao, X. Li, and G. Zhao, “Dual-cross central difference network for face anti-spoofing,” CoRR, vol. abs/2105.01290, 2021. [Online]. Available: [link]
Z. Yu, C. Zhao, Z. Wang, Y. Qin, Z. Su, X. Li, F. Zhou, and G. Zhao, “Searching central difference convolutional networks for face anti-spoofing,” CoRR, vol. abs/2003.04092, 2020.
Q. Zhou, K.-Y. Zhang, T. Yao, X. Lu, R. Yi, S. Ding, and L. Ma, “Instance-aware domain generalization for face anti-spoofing,” 2023.
B. M. Le and S. S. Woo, “Gradient alignment for cross-domain face anti-spoofing,” 2024.
H. Li, W. Li, H. Cao, S. Wang, F. Huang, and A. C. Kot, “Unsupervised domain adaptation for face anti-spoofing,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 7, pp. 1794–1809, 2018.
J. Wang, J. Zhang, Y. Bian, Y. Cai, C. Wang, and S. Pu, “Self-domain adaptation for face anti-spoofing,” CoRR, vol. abs/2102.12129, 2021. [Online]. Available: [link]
M. Singh and A. S. Arora, “A novel face liveness detection algorithm with multiple liveness indicators,” Wireless Personal Communications, vol. 100, no. 4, pp. 1677–1687, Jun 2018.
I. Sluganovic, M. Roeschlin, K. Rasmussen, and I. Martinovic, “Using reflexive eye movements for fast challenge-response authentication,” in CCS ’16: ACM SIGSAC Conference on Computer and Communications Security, 10 2016, pp. 1056–1067.
M. Shen, Z. Liao, L. Zhu, R. Mijumbi, X. Du, and J. Hu, “Iritrack: Liveness detection using irises tracking for preventing face spoofing attacks,” 2018. [Online]. Available: [link]
P. McShane and D. Stewart, “Challenge based visual speech recognition using deep learning,” in 2017 12th Int. Conf. for Internet Technology and Secured Transactions (ICITST), 2017, pp. 405–410.
E. Uzun, S. Chung, I. Essa, and W. Lee, “rtCaptcha: A real-time captcha based liveness detection system,” in Conference: The Network and Distributed System Security Symposium (NDSS), 02 2018.
C.-L. Chou, “Presentation attack detection based on score level fusion and challenge-response technique,” The Journal of Supercomputing, vol. 77, 05 2021.
M. De Marsico, M. Nappi, D. Riccio, and J.-L. Dugelay, “Moving face spoofing detection via 3D projective invariants,” in 2012 5th IAPR Int. Conf. on Biometrics (ICB). IEEE, 2012, pp. 73–78.
D. Riccio and J.-L. Dugelay, “Geometric invariants for 2d/3d face recognition,” Pattern Recognit. Lett., vol. 28, pp. 1907–1914, 2007. [Online]. Available: [link]
D. E. King, “Dlib-ml: A machine learning toolkit,” Journal of Machine Learning Research, vol. 10, pp. 1755–1758, 2009.
L. Li, Z. Xia, J. Wu, L. Yang, and H. Han, “Face presentation attack detection based on optical flow and texture analysis,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 4, pp. 1455–1467, 2022.
S. Chen, A. Pande, and P. Mohapatra, “Sensor-assisted facial recognition: an enhanced biometric authentication system for smartphones,” in 12th Annual International Conference on Mobile Systems, Applications, and Services, 2014, pp. 109–122.
D. Gollmann, “Computer security,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 5, pp. 544–554, 2010.
S. Z. S. Idrus, E. Cherrier, C. Rosenberger, and J.-J. Schwartzmann, “A review on authentication methods,” Australian Journal of Basic and Applied Sciences, vol. 7, no. 5, pp. 95–107, 2013.
J. Yan, Z. Zhang, Z. Lei, D. Yi, and S. Z. Li, “Face liveness detection by exploring multiple scenic clues,” 2012 12th Int. Conf. on Control Automation Robotics & Vision (ICARCV), pp. 188–193, 2012.
M. Killioğlu, M. Taşkiran, and N. Kahraman, “Anti-spoofing in face recognition with liveness detection using pupil tracking,” in 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2017, pp. 000 087–000 092.
M. Singh and A. Arora, “A robust anti-spoofing technique for face liveness detection with morphological operations,” Optik, vol. 139, pp. 347–354, 2017.
M. Mohzary, K. J. Almalki, B.-Y. Choi, and S. Song, “Your eyes show what your eyes see (y-eyes): Challenge-response anti-spoofing method for mobile security using corneal specular reflections,” in 1st Workshop on Security and Privacy for Mobile AI, 2021, p. 25–30.
Y. Liu, Y. Tai, J. Li, S. Ding, C. Wang, F. Huang, D. Li, W. Qi, and R. Ji, “Aurora guard: Real-time face anti-spoofing via light reflection,” CoRR, vol. abs/1902.10311, 2019. [Online]. Available: [link]
J. M. D. Martino, Q. Qiu, T. Nagenalli, and G. Sapiro, “Liveness detection using implicit 3D features,” CoRR, vol. abs/1804.06702, 2018. [Online]. Available: [link]
W. Xu, J. Liu, S. Zhang, Y. Zheng, F. Lin, J. Han, F. Xiao, and K. Ren, “Rface: Anti-spoofing facial authentication using cots rfid,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1–10.
M. Ezz, M. Ayman, and A. Elshenawy Elsefy, “Challenge-response emotion authentication algorithm using modified horizontal deep learning,” Intelligent Automation and Soft Computing, vol. 35, pp. 3659–3675, 09 2022.
Z. Ming, J. Chazalon, M. M. Luqman, M. Visani, and J.-C. Burie, “Facelivenet: End-to-end networks combining face verification with interactive facial expression-based liveness detection,” in 2018 24th Int. Conf. on Pattern Recognition (ICPR). IEEE, 2018, pp. 3507–3512.
Y. Li, Z. Wang, Y. Li, R. Deng, B. Chen, W. Meng, and H. Li, “A closer look tells more: A facial distortion based liveness detection for face authentication,” in Asia CCS ’19: ACM Asia Conference on Computer and Communications Security, 07 2019, pp. 241–246.
A. Castelblanco, E. Rivera, J. Solano, L. Tengana, C. López, and M. Ochoa, “Dynamic face authentication systems: Deep learning verification for camera close-up and head rotation paradigms,” Comput. Secur., vol. 115, no. C, apr 2022.
X. Tan, Y. Li, J. Liu, and L. Jiang, “Face liveness detection from a single image with sparse low rank bilinear discriminative model,” in Computer Vision – ECCV 2010, K. Daniilidis, P. Maragos, and N. Paragios, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 504–517.
A. Anjos and S. Marcel, “Countermeasures to photo attacks in face recognition: A public database and a baseline,” in 2011 International Joint Conference on Biometrics (IJCB), 2011, pp. 1–7.
Z. Zhang, J. Yan, S. Liu, Z. Lei, D. Yi, and S. Z. Li, “A face antispoofing database with diverse attacks,” in 2012 5th IAPR Int. Conf. on Biometrics (ICB), 2012, pp. 26–31.
I. Chingovska, A. Anjos, and S. Marcel, “On the effectiveness of local binary patterns in face anti-spoofing,” in 2012 BIOSIG - Int. Conf. of Biometrics Special Interest Group (BIOSIG), 2012, pp. 1–7.
D. Wen, H. Han, and A. K. Jain, “Face spoof detection with image distortion analysis,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 746–761, 2015.
K. Patel, H. Han, and A. K. Jain, “Secure face unlock: Spoof detection on smartphones,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 10, pp. 2268–2283, 2016.
A. Agarwal, D. Yadav, N. Kohli, R. Singh, M. Vatsa, and A. Noore, “Face presentation attack with latex masks in multispectral videos,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017.
Z. Boulkenafet, J. Komulainen, L. Li, X. Feng, and A. Hadid, “OULU-NPU: A mobile face presentation attack database with real-world variations,” in 2017 12th IEEE Int. Conf. on Automatic Face & Gesture Recognition (FG 2017), 2017, pp. 612–618.
Y. Liu, A. Jourabloo, and X. Liu, “Learning deep models for face anti-spoofing: Binary or auxiliary supervision,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
Y. Liu, J. Stehouwer, A. Jourabloo, and X. Liu, “Deep tree learning for zero-shot face anti-spoofing,” in 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4675–4684.
Z. Mostaani, A. George, G. Heusch, D. Geissbühler, and S. Marcel, “The high-quality wide multi-channel attack (HQ-WMCA) database,” CoRR, vol. abs/2009.09703, 2020.
Z. Wang, Z. Yu, C. Zhao, X. Zhu, Y. Qin, Q. Zhou, F. Zhou, and Z. Lei, “Deep spatial gradient and temporal depth learning for face anti-spoofing,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 5041–5050.
Y. Zhang, Z. Yin, Y. Li, G. Yin, J. Yan, J. Shao, and Z. Liu, “CelebA-spoof: Large-scale face anti-spoofing dataset with rich annotations,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16. Springer, 2020, pp. 70–85.
D. Wang, J. Guo, Q. Shao, H. He, Z. Chen, C. Xiao, A. Liu, S. Escalera, H. J. Escalante, Z. Lei et al., “Wild face anti-spoofing challenge 2023: Benchmark and results,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 6379–6390.
Z. Boulkenafet, J. Komulainen, and A. Hadid, “Face antispoofing using speeded-up robust features and fisher vector encoding,” IEEE Signal Processing Letters, vol. 24, no. 2, pp. 141–145, 2017.
T. de Freitas Pereira, A. Anjos, J. M. De Martino, and S. Marcel, “Lbp-top based countermeasure against face spoofing attacks,” in Computer Vision - ACCV 2012 Workshops, J.-I. Park and J. Kim, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 121–132.
J. Komulainen, A. Hadid, and M. Pietikäinen, “Context based face anti-spoofing,” in 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2013, pp. 1–8.
Z. Yu, C. Zhao, Z. Wang, Y. Qin, Z. Su, X. Li, F. Zhou, and G. Zhao, “Searching central difference convolutional networks for face anti-spoofing,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 5294–5304.
Z. Yu, Y. Qin, H. Zhao, X. Li, and G. Zhao, “Dual-cross central difference network for face anti-spoofing,” CoRR, vol. abs/2105.01290, 2021. [Online]. Available: [link]
Z. Yu, C. Zhao, Z. Wang, Y. Qin, Z. Su, X. Li, F. Zhou, and G. Zhao, “Searching central difference convolutional networks for face anti-spoofing,” CoRR, vol. abs/2003.04092, 2020.
Q. Zhou, K.-Y. Zhang, T. Yao, X. Lu, R. Yi, S. Ding, and L. Ma, “Instance-aware domain generalization for face anti-spoofing,” 2023.
B. M. Le and S. S. Woo, “Gradient alignment for cross-domain face anti-spoofing,” 2024.
H. Li, W. Li, H. Cao, S. Wang, F. Huang, and A. C. Kot, “Unsupervised domain adaptation for face anti-spoofing,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 7, pp. 1794–1809, 2018.
J. Wang, J. Zhang, Y. Bian, Y. Cai, C. Wang, and S. Pu, “Self-domain adaptation for face anti-spoofing,” CoRR, vol. abs/2102.12129, 2021. [Online]. Available: [link]
M. Singh and A. S. Arora, “A novel face liveness detection algorithm with multiple liveness indicators,” Wireless Personal Communications, vol. 100, no. 4, pp. 1677–1687, Jun 2018.
I. Sluganovic, M. Roeschlin, K. Rasmussen, and I. Martinovic, “Using reflexive eye movements for fast challenge-response authentication,” in CCS ’16: ACM SIGSAC Conference on Computer and Communications Security, 10 2016, pp. 1056–1067.
M. Shen, Z. Liao, L. Zhu, R. Mijumbi, X. Du, and J. Hu, “Iritrack: Liveness detection using irises tracking for preventing face spoofing attacks,” 2018. [Online]. Available: [link]
P. McShane and D. Stewart, “Challenge based visual speech recognition using deep learning,” in 2017 12th Int. Conf. for Internet Technology and Secured Transactions (ICITST), 2017, pp. 405–410.
E. Uzun, S. Chung, I. Essa, and W. Lee, “rtCaptcha: A real-time captcha based liveness detection system,” in Conference: The Network and Distributed System Security Symposium (NDSS), 02 2018.
C.-L. Chou, “Presentation attack detection based on score level fusion and challenge-response technique,” The Journal of Supercomputing, vol. 77, 05 2021.
M. De Marsico, M. Nappi, D. Riccio, and J.-L. Dugelay, “Moving face spoofing detection via 3D projective invariants,” in 2012 5th IAPR Int. Conf. on Biometrics (ICB). IEEE, 2012, pp. 73–78.
D. Riccio and J.-L. Dugelay, “Geometric invariants for 2d/3d face recognition,” Pattern Recognit. Lett., vol. 28, pp. 1907–1914, 2007. [Online]. Available: [link]
D. E. King, “Dlib-ml: A machine learning toolkit,” Journal of Machine Learning Research, vol. 10, pp. 1755–1758, 2009.
L. Li, Z. Xia, J. Wu, L. Yang, and H. Han, “Face presentation attack detection based on optical flow and texture analysis,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 4, pp. 1455–1467, 2022.
S. Chen, A. Pande, and P. Mohapatra, “Sensor-assisted facial recognition: an enhanced biometric authentication system for smartphones,” in 12th Annual International Conference on Mobile Systems, Applications, and Services, 2014, pp. 109–122.
Publicado
30/09/2024
Como Citar
KAMAROWSKI, Bruno; ALMEIDA, Raul; BIESSECK, Bernardo; GRANADA, Roger; COELHO, Luiz; MENOTTI, David.
Hybrid method for active face anti-spoofing based on close-up challenge. In: WORKSHOP DE TRABALHOS EM ANDAMENTO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 37. , 2024, Manaus/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 105-110.
DOI: https://doi.org/10.5753/sibgrapi.est.2024.31653.
