[1] M. Wang and W. Deng, "Deep face recognition: A survey," Neurocomputing, vol. 429, pp. 215-244, 2021.
[2] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, "Intriguing properties of neural networks," in Proceedings of the International Conference on Learning Representations, 2014.
[3] A. Ilyas, L. Engstrom, A. Athalye, and J. Lin, "Black-box adversarial attacks with limited queries and information," in Proceedings of the 35th International Conference on Machine Learning. PMLR, 2018, pp. 2137-2146.
[4] S. Li, A. Neupane, S. Paul, C. Song, S. V. Krishnamurthy, A. K. Roy-Chowdhury, and A. Swami, "Stealthy adversarial perturbations against real-time video classification systems," in Proceedings of the Symposium on Network and Distributed Systems Security (NDSS), 2019.
[5] Y. Cao, X. Xiao, R. Sun, D. Wang, M. Xue, and S. Wen, "Stylefool: Fooling video classification systems via style transfer," in 2023 IEEE Symposium on Security and Privacy (SP). IEEE, 2023, pp. 1631-1648.
[6] S. Marcel, M. S. Nixon, J. Fierrez, and N. Evans, Handbook of biometric anti-spoofing: Presentation attack detection. Springer, 2019, vol. 2.
[7] R. Ramachandra and C. Busch, "Presentation attack detection methods for face recognition systems: A comprehensive survey," ACM Computing Surveys, vol. 50, no. 1, pp. 1-37, 2017.
[8] J. Komulainen, A. Hadid, and M. Pietikainen, "Context based face anti-spoofing," in Proceedings of the International Conference on Biometrics: Theory, Applications and Systems, 2013.
[9] Z. Boulkenafet, J. Komulainen, and A. Hadid, “Face spoofing detection using colour texture analysis,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 8, pp. 1818–1830, 2016.
[10] J.-W. Li, "Eye blink detection based on multiple gabor response waves," in 2008 International Conference on Machine Learning and Cybernetics, vol. 5. IEEE, 2008, pp. 2852-2856.
[11] W. Bao, H. Li, N. Li, and W. Jiang, "A liveness detection method for face recognition based on optical flow field," in 2009 International Conference on Image Analysis and Signal Processing. IEEE, 2009, pp. 233-236.
[12] T. de Freitas Pereira, J. Komulainen, A. Anjos, J. M. De Martino, A. Hadid, M. Pietikainen, and S. Marcel, "Face liveness detection using dynamic texture," EURASIP Journal on Image and Video Processing, vol. 2014, no. 1, p. 2, 2014.
[13] D. Yi, Z. Lei, Z. Zhang, and S. Z. Li, “Face anti-spoofing: Multi-spectral approach,” Handbook of Biometric Anti-Spoofing: Trusted Biometrics under Spoofing Attacks, pp. 83–102, 2014.
[14] X. Song, X. Zhao, L. Fang, and T. Lin, "Discriminative representation combinations for accurate face spoofing detection," Pattern Recognition, vol. 85, pp. 220-231, 2019.
[15] 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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 5295–5305.
[16] Z. Yu, X. Li, X. Niu, J. Shi, and G. Zhao, “Face anti-spoofing with human material perception,” in Proceedings of European Conference on Computer Vision, 2020.
[17] X. Yang, W. Luo, L. Bao, Y. Gao, D. Gong, S. Zheng, Z. Li, and W. Liu, “Face anti-spoofing: Model matters, so does data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 3507–3516.
[18] Y. Atoum, Y. Liu, A. Jourabloo, and X. Liu, “Face anti-spoofing using patch and depth-based cnns,” in Proceedings of IEEE International Joint Conference on Biometrics, 2017.
[19] N. Erdogmus and S. Marcel, "Spoofing face recognition with 3d masks," IEEE transactions on information forensics and security, vol. 9, no. 7, pp. 1084-1097, 2014.
[20] X. Zhang, W. Wu, Y. Huang, X. Yang, and M. Gou, "Antheraea peryi silk fibroin bioinks for digital light processing 3d printing," International Journal of Bioprinting, vol. 9, no. 5, 2023.