Studi Literatur Presentation Attack dan Set Data Anti-Spoof Wajah

I Kadek Dendy Senapartha; Gabriel Indra Widi Tamtama

doi:10.15294/jte.v14i1.36108

Studi Literatur Presentation Attack dan Set Data Anti-Spoof Wajah

I Kadek Dendy Senapartha⁽¹⁾, Gabriel Indra Widi Tamtama⁽²⁾,

DOI: https://doi.org/10.15294/jte.v14i1.36108

(1) Universitas Kristen Duta Wacana
(2) Universitas Kristen Duta Wacana

Abstract

Face anti-spoof systems are needed in facial recognition systems to ward off attacks that present fake faces in front of the camera or image capture sensor (presentation attack). To build the system, a data set is needed to build a classification model that distinguishes the authenticity of the face of the input image received by the system. In the past decade anti-face spoof research has produced many data sets that are public, but often researchers need time to build or use the right public data sets that are used to build facial anti-spoof models. This article conducts a literature study of public data sets using a systematic literature review method to find out the types of attacks that appear on the facial anti-spoof system, the development process, evolution, and availability of facial anti-spoof data sets. From the search and selection results based on the specified criteria, there were 42 primary research manuscripts in the period 2010 to 2021. The results of the literature study found that there were three trends in the development of anti-spoof facial data sets, namely, 1) data sets with a very large number, 2) datasets with different types of facial samples, and 3) datasets constructed with various devices and sensors. These various public data sets can be accessed freely but with special rules such as agreeing to an end user license agreement document from the researcher or the institution that owns the data set. However, there are also datasets that cannot be accessed due to invalid URLs or due to special rules from the cloud storage service provider where the datasets are stored.

Keywords

face anti-spoof dataset; face anti-spoof model; face anti-spoof system; face recognition system; presentation attack

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References

W. W. Bledsoe, “The Model Method in Facial Recognition,” Palo Alto, California, 1964.

A. K. Jain, A. Ross, and S. Pankanti, “Biometrics: A tool for information security,” IEEE Transactions on Information Forensics and Security, vol. 1, no. 2, pp. 125–143, 2006, doi: 10.1109/TIFS.2006.873653.

S. Abbass, “A Model for Trust-building in E-commerce from Consumer to Consumer Perspective in KSA,” International Journal of e-Education, e-Business, e-Management and e-Learning, vol. 1, no. 3, pp. 223–227, 2011, doi: 10.7763/ijeeee.2011.v1.35.

L. Souza, L. Oliveira, M. Pamplona, and J. Papa, “How far did we get in face spoofing detection?,” Engineering Applications of Artificial Intelligence, vol. 72, no. December 2017, pp. 368–381, 2018, doi: 10.1016/j.engappai.2018.04.013.

Z. Yu, Y. Qin, X. Li, C. Zhao, Z. Lei, and G. Zhao, “Deep Learning for Face Anti-Spoofing: A Survey,” pp. 1–25, 2021, [Online]. Available: http://arxiv.org/abs/2106.14948

E. H. Gebre and E. Morales, “How ‘accessible’ is open data?: Analysis of context-related information and users’ comments in open datasets,” Information and Learning Science, vol. 121, no. 1–2, pp. 19–36, 2020, doi: 10.1108/ILS-08-2019-0086.

B. Kitchenham, “Procedures for Performing Systematic Reviews,” Keele, Staffs ST5 5BG, UK, 2004.

R. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, and J. Ortega-Garcia, “Deepfakes and beyond: A Survey of face manipulation and fake detection,” Information Fusion, vol. 64, pp. 131–148, 2020, doi: 10.1016/j.inffus.2020.06.014.

G. Goswami, A. Agarwal, N. Ratha, R. Singh, and M. Vatsa, “Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition,” International Journal of Computer Vision, vol. 127, no. 6–7, pp. 719–742, 2019, doi: 10.1007/s11263-019-01160-w.

A. Liu et al., “Cross-ethnicity face anti-spoofing recognition challenge: A review,” IET Biometrics, vol. 10, no. 1, pp. 24–43, 2021, doi: 10.1049/bme2.12002.

R. Ramachandra and C. Busch, “Presentation attack detection methods for face recognition systems: A comprehensive survey,” ACM Computing Surveys, vol. 50, no. 1, 2017, doi: 10.1145/3038924.

S. Jia, G. Guo, and Z. Xu, “A survey on 3D mask presentation attack detection and countermeasures,” Pattern Recognition, vol. 98, p. 107032, 2020, doi: 10.1016/j.patcog.2019.107032.

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, doi: 10.1109/TIFS.2016.2578288.

B. Peixoto, C. Michelassi, and A. Rocha, “Face liveness detection under bad illumination conditions,” Proceedings - International Conference on Image Processing, ICIP, no. July, pp. 3557–3560, 2011, doi: 10.1109/ICIP.2011.6116484.

N. Kose and J. L. Dugelay, “Shape and texture based countermeasure to protect face recognition systems against mask attacks,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 111–116, 2013, doi: 10.1109/CVPRW.2013.24.

S. Liu, B. Yang, P. C. Yuen, and G. Zhao, “A 3D Mask Face Anti-Spoofing Database with Real World Variations,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1551–1557, 2016, doi: 10.1109/CVPRW.2016.193.

R. H. Vareto, A. Marcia Saldanha, and W. R. Schwartz, “The Swax Benchmark: Attacking Biometric Systems with Wax Figures,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2020-May, pp. 986–990, 2020, doi: 10.1109/ICASSP40776.2020.9053946.

S. Zhang et al., “CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 2, no. 2, pp. 182–193, 2020, doi: 10.1109/tbiom.2020.2973001.

A. Liu et al., “Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection,” 2021, [Online]. Available: http://arxiv.org/abs/2104.06148

X. Tan, Y. Li, J. Liu, and L. Jiang, “Face Liveness Detection from A Single Image with Sparse Low Rank Bilinear Discriminative Model,” European Conference on Computer Vision, vol. VI, pp. 504–517, 2010, doi: 10.1007/978-3-642-15567-3_37.

Z. Zhang, J. Yan, S. Liu, Z. Lei, D. Yi, and S. Z. Li, “A face antispoofing database with diverse attacks,” Proceedings - 2012 5th IAPR International Conference on Biometrics, ICB 2012, pp. 26–31, 2012, doi: 10.1109/ICB.2012.6199754.

A. Anjos and S. Marcel, “Counter-measures to photo attacks in face recognition: A public database and a baseline,” 2011 International Joint Conference on Biometrics, IJCB 2011, 2011, doi: 10.1109/IJCB.2011.6117503.

I. Chingovska, A. Anjos, and S. Marcel, “On the effectiveness of local binary patterns in face anti-spoofing,” Proceedings of the International Conference of the Biometrics Special Interest Group, BIOSIG 2012, 2012.

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, doi: 10.1109/TIFS.2015.2400395.

A. Pinto, W. R. Schwartz, H. Pedrini, and A. D. R. Rocha, “Using visual rhythms for detecting video-based facial spoof attacks,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 5, pp. 1025–1038, 2015, doi: 10.1109/TIFS.2015.2395139.

A. Costa-Pazo, S. Bhattacharjee, E. Vazquez-Fernandez, and S. Marcel, “The REPLAY-MOBILE face presentation-attack database,” Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI), vol. P-260, no. September, 2016, doi: 10.1109/BIOSIG.2016.7736936.

I. Manjani, S. Tariyal, M. Vatsa, R. Singh, and A. Majumdar, “Detecting silicone mask-based presentation attack via deep dictionary learning,” IEEE Transactions on Information Forensics and Security, vol. 12, no. 2, pp. 1713–1723, 2017, doi: 10.1109/TIFS.2017.2676720.

Z. Boulkenafet, J. Komulainen, L. Li, X. Feng, and A. Hadid, “OULU-NPU: A Mobile Face Presentation Attack Database with Real-World Variations,” Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heteroge, no. June, pp. 612–618, 2017, doi: 10.1109/FG.2017.77.

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, doi: 10.1109/TIFS.2018.2801312.

Y. Liu, A. Jourabloo, and X. Liu, “Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 389–398, 2018, doi: 10.1109/CVPR.2018.00048.

S. Jia, X. Li, C. Hu, G. Guo, and Z. Xu, “3D Face Anti-Spoofing with Factorized Bilinear Coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 10, pp. 4031–4045, 2021, doi: 10.1109/TCSVT.2020.3044986.

Y. Liu, J. Stehouwer, A. Jourabloo, and X. Liu, “Deep tree learning for zero-shot face anti-spoofing,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 4675–4684, 2019, doi: 10.1109/CVPR.2019.00481.

Y. Zhang et al., “CelebA-Spoof: Large-Scale Face Anti-spoofing Dataset with Rich Annotations,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12357 LNCS, pp. 70–85, 2020, doi: 10.1007/978-3-030-58610-2_5.

W. R. Almeida et al., “Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function,” PLoS ONE, vol. 15, no. 9 september, pp. 1–24, 2020, doi: 10.1371/journal.pone.0238058.

Z. Yu, J. Wan, Y. Qin, X. Li, S. Z. Li, and G. Zhao, “NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 9, pp. 3005–3023, 2021, doi: 10.1109/TPAMI.2020.3036338.

X. Chen, S. Xu, Q. Ji, and S. Cao, “A Dataset and Benchmark towards Multi-Modal Face Anti-Spoofing under Surveillance Scenarios,” IEEE Access, vol. 9, pp. 28140–28155, 2021, doi: 10.1109/ACCESS.2021.3052728.

D. Timoshenko, K. Simonchik, V. Shutov, P. Zhelezneva, and V. Grishkin, “Large Crowdcollected Facial Anti-Spoofing Dataset,” 12th International Conference on Computer Science and Information Technologies, CSIT 2019, pp. 123–126, 2019, doi: 10.1109/CSITechnol.2019.8895208.

A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: Illumination cone models for face recognition under variable lighting and pose,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 643–660, 2001, doi: 10.1109/34.927464.

Tenba Group, “How to Get a Baidu Account,” 2022. https://tenbagroup.com/how-to-get-a-baidu-account/ (accessed Apr. 08, 2022).

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