Focus Challenge Based Presentation Attack Detection in Face Authentication Systems Using Generative Adversarial Network
Keywords:
GAN, Face Recognition Systems, Deep LearningAbstract
Presentation based deceiving of biometric recognition especially face recognition systems have become very common. Detecting such attacks is very important to ensure attack resilience of authentication systems built on biometric recognition. This work proposes a focus challenge based presentation attack detection for Face authentication systems using Generative Adversarial Network (GAN). The difference between the focus varying GAN generated images to the real images is compared in terms of their Deep learning group signature to detect fakes. The focus challenge is very shift giving almost no chance for presentation attack to deceive it. The proposed GAN based presentation attack detection system is very resilient to presentation attacks with noninvasive detection process. Testing under different environmental conditions, the proposed solution is found to have less than 1.74 Attack Presentation Classification Error Rate (APCER) which is atleast 1.3 times less than existing works.
Downloads
References
A. Mohamed, M. M. Nagah, M. G. Abdelmonem, M. Y. Ahmed, M. El-Sahhar and F. H. Ismail, "Face Liveness Detection Using a sequential CNN technique," 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), 2021, pp. 1483-1488,.
Y. Zhang, Z. Yin, Y. Li, G. Yin, J. Yan, J. Shao, and Z. Liu, “Celebaspoof: Large-scale face anti-spoofing dataset with rich annotations,” arXiv preprint arXiv:2007.12342, 2020.
Kim, S.; Ban, Y.; Lee, S. Face liveness detection using defocus. Sensors 2015, 15, 1537–1563.
De Souza, G.B.; Da Silva Santos, D.F.; Pires, R.G.; Marana, A.N.; Papa, J.P. Deep texture features for robust face spoofing detection. IEEE Trans. Circuits Syst. II Express Briefs 2017, 64, 1–5.
Parveen, S.; Ahmad, S.M.S.; Abbas, N.H.; Adnan,W.A.W.; Hanafi, M.; Naeem, N. Face liveness detection using dynamic local ternary pattern (DLTP). Computers 2016, 5, 10.
Akhtar, Z.; Foresti, G.L. Face spoof attack recognition using discriminative image patches. J. Electr. Comput. Eng. 2016, 2016, 1–14.
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.
S. Tirunagari, N. Poh, D. Windridge, A. Iorliam, N. Suki, and A. T. S. Ho, “Detection of face spoofing using visual dynamics,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 762–777, 2015.
Z. Boulkenafet, J. Komulainen, and A. Hadid, “Face antispoofing based on color texture analysis,” in Proceedings of the IEEE International Conference on Image Processing (ICIP ’15), pp. 2636–2640, Quebec City, Canada, September 2015.
J. Zhou, K. Shu, P. Liu, J. Xiang and S. Xiong, "Face Anti-Spoofing Based on Dynamic Color Texture Analysis Using Local Directional Number Pattern," in 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021 pp. 4221-4228.
Li, X.; Komulainen, J.; Zhao, G.; Yuen, P.C.; Pietikäinen, M. Generalized face anti-spoofing by detecting pulse from face videos. In Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 4–8 December 2016; pp. 4244–4249
Hasan, Md Rezwan & Mahmud, S & Li, Xiang. (2019). Face Anti-Spoofing Using Texture-Based Techniques and Filtering Methods. Journal of Physics: Conference Series. 1229. 012044. 10.1088/1742-6596/1229/1/012044.
Cai, Rizhao & Li, Zhi & Wan, Renjie & Li, Haoliang & Hu, Yongjian & Kot, Alex. (2021). Learning Meta Pattern for Face Anti-Spoofing.
W. Zheng, M. Yue, S. Zhao and S. Liu, "Attention-Based Spatial-Temporal Multi-Scale Network for Face Anti-Spoofing," in IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 3, pp. 296-307, July 2021
Song, Xiao & Zhao, Xu & Lin, Tianwei. (2017). Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid Coding Micro-Texture Features. 96-100. 10.1109/ICIP.2017.8296250.
R. Cai, H. Li, S. Wang, C. Chen, and A. C. Kot, "DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing," IEEE Transactions on Information Forensics and Security, vol. 16, pp. 937-951, 2020
Z. Yu, X. Li, X. Niu, J. Shi, and G. Zhao, "Face anti-spoofing with human material perception," 2020, arXiv:2007.02157
Tu, Xiaoguang & Zhang, Hengsheng & Xie, Mei & Luo, Yao & Zhang, Yuefei & Ma, Zheng. (2019). Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture.
G. Wang, C. Lan, H. Han, S. Shan and X. Chen, "Multi-modal face presentation attack detection via spatial and channel attentions," IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 1584-1590
Y. Liu, Y. Tai, J. Li, S. Ding, C. Wang, F. Huang, and R. Ji, "Aurora guard: Real-time face anti-spoofing via light reflection," 2019, arXiv:1902.10311
Chou, Chao-Lung. (2021). Presentation attack detection based on score level fusion and challenge-response technique. The Journal of Supercomputing. 77. 10.1007/s11227-020-03461-1.
Melnikov A, Akhunzyanov R, Oleg K, Luckyanets E (2015) Audiovisual liveness detection. In: International Conference on Image Analysis and Processing (ICIAP 2015), vol 9280, pp 643–652
Boutellaa E, Boulkenafet Z, Komulainen J, Hadid A (2016) Audiovisual synchrony assessment for replay attack detection in talking face biometrics. Multimed Tools Appl 75(9):5329–5343
Benitez-Quiroz C.F., Srinivasan R., Martinez A.M. EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Las Vegas, NV, USA. 26 June–1 July 2016; pp. 5562–5570.
Tao S.Y., Martinez A.M. Compound facial expressions of emotion. Natl. Acad. Sci. 2014;111:E1454–E1462
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in CVPR, 2001, pp. 511–518
Asthana, S. Zafeiriou, S. Cheng, and M. Pantic, “Robust discriminative response map fitting with constrained local models,” in CVPR, 2013
Z. Boulkenafet, J. Komulainen, L. Li, X. Feng, and A. Hadid, “Oulu-npu: A mobile face presentation attack database with real-world variations,” IEEE International Conference on Automatic Face & Gesture Recognition (FG), 2017, pp. 612–618
Z. Wang, Z. Yu, C. Zhao, X. Zhu, Y. Qin, Q. Zhou, and Z. Lei, "Deep spatial gradient and temporal depth learning for face anti-spoofing," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 5042-5051.
J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D.Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial networks. Advances in Neural Informa-tion Processing Systems, 3:2672–2680, 2014
Z. -H. Li, Y. Hou and H. -B. Liu, “Alignment of face images based on SIFT feature,” 2014 International Conference on Machine Learning and Cybernetics, 2014, pp. 597-600
Khare, S. ., & Badholia, A. . (2023). BLA2C2: Design of a Novel Blockchain-based Light-Weight Authentication & Access Control Layer for Cloud Deployments. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 283–294. https://doi.org/10.17762/ijritcc.v11i3.6359
Verma, D. N. . (2022). Access Control-Based Cloud Storage Using Role-Fully Homomorphic Encryption Scheme. Research Journal of Computer Systems and Engineering, 3(1), 78–83. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/46
Yadav, N., Saini, D.K.J.B., Uniyal, A., Yadav, N., Bembde, M.S., Dhabliya, D. Prediction of Omicron cases in India using LSTM: An advanced approach of artificial intelligence (2023) Journal of Interdisciplinary Mathematics, 26 (3), pp. 361-370.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
