№2, 2023

Leyla G. Muradkhanli, Parviz A. Namazli

The paper suggests a technique that uses convolutional neural network (CNN) to identify fraudulent facial manipulation. The proposed method comprises teaching an intricate neural network using a comprehensive compilation of genuine and fake facial images. The structure of CNN includes several layers of convolution and pooling, which enable it to identify distinguishing features in the input images. Following its training, the model is employed to differentiate a presented facial image into either authentic or fraudulent. To determine the efficacy of the proposed technique, a standardized data set for identifying counterfeit or altered facial attributes was used. The suggested method presents various benefits compared to current techniques for detecting face spoofing. To start with, utilizing a deep CNN empowers the model to acquire complex and discerning characteristics from the input images, thus augmenting the precision of the categorization mission. Additionally, the suggested method is effective in terms of computational requirements, enabling its utilization in real-time scenarios. The proposed methodology is able to withstand a range of fraudulent tactics used on facial recognition systems, such as print and replay attacks. The findings from this study aid in the progression of face recognition technology by enhancing the accuracy and dependability of fraud detection systems. These improved systems have practical applications in security measures, biometric identification, and digital criminal investigations. The suggested method could substantially enhance the dependability and safety of facial recognition systems, consequently boosting their functional value and credibility (pp.40-46).

Keywords:Convolutional neural networks, Deep learning, Facial recognition, Image manipulation, Evaluation metrics, Image processing, Security, Authentication

Arashloo, S. R., Kittler, J., & Christmas, W. (2015). Face spoofing detection based on multiple descriptor fusion using multiscale dynamic binarized statistical image features. IEEE Transactions on Information Forensics and Security, 10(11), 2396-2407.

Bashier, H. K., Lau, S. H., Han, P. Y., Ping, L. Y., & Li, C. M. (2014). Face spoofing detection using local graph structure. In 2014 International Conference on Computer, Communications and Information Technology (CCIT 2014), pp. 270-273. Atlantis Press.

Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., & Hadid, A. (2017, May). OULU-NPU: A mobile face presentation attack database with real-world variations. In 2017 12th IEEE international conference on automatic face & gesture recognition (FG 2017), pp. 612-618. https://doi.org/10.1109/FG.2017.77

Chen, H., Hu, G., Lei, Z., Chen, Y., Robertson, N. M., & Li, S. Z. (2019). Attention-based two-stream convolutional networks for face spoofing detection. IEEE Transactions on Information Forensics and Security, 15, 578-593.

Galbally, J., Marcel, S., & Fierrez, J. (2013). Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition. IEEE transactions on image processing, 23(2), 710-724.

Hassan, R. J., & Abdulazeez, A. M. (2021). Deep learning convolutional neural network for face recognition: A review. International Journal of Science and Business, 5(2), 114-127.

Komulainen, J., Hadid, A., & Pietikäinen, M. (2013, September). Context based face anti-spoofing. In 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1-8.

Kumar, S., Singh, S., & Kumar, J. (2017, May). A comparative study on face spoofing attacks. In 2017 International Conference on Computing, Communication and Automation (ICCCA), pp. 1104-1108.

Liu, Y., Jourabloo, A., & Liu, X. (2018). Learning deep models for face anti-spoofing: Binary or auxiliary supervision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 389-398.

Määttä, J., Hadid, A., & Pietikäinen, M. (2011, October). Face spoofing detection from single images using micro-texture analysis. In 2011 international joint conference on Biometrics (IJCB), pp. 1-7.

Pinto, A., Pedrini, H., Schwartz, W. R., & Rocha, A. (2015). Face spoofing detection through visual codebooks of spectral temporal cubes. IEEE Transactions on Image Processing, 24(12), pp.4726-4740.

Seal, A., Ganguly, S., Bhattacharjee, D., Nasipuri, M., & Basu, D. K. (2013). Automated thermal face recognition based on minutiae extraction. International Journal of Computational Intelligence Studies, 2(2), 133-156.

Singh, R., & Om, H. (2013, December). An overview of face recognition in an unconstrained environment. In 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), pp. 672-677.

Strobl, K. H., Mair, E., & Hirzinger, G. (2011, May). Image-based pose estimation for 3-D modeling in rapid, hand-held motion. In 2011 IEEE International Conference on Robotics and Automation, pp. 2593-2600.

Tan, X., Li, Y., Liu, J., & Jiang, L. (2010). Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Model. ECCV (6), 6316, pp.504-517.

Wang, Z., Zhao, C., Qin, Y., Zhou, Q., Qi, G., Wan, J., & Lei, Z. (2018). Exploiting temporal and depth information for multi-frame face anti-spoofing. arXiv preprint arXiv:1811.05118. https://doi.org/10.48550/arXiv.1811.05118

Wen, Y., Zhao, Y., & Wang, Z. (2018). Face spoof detection based on convolutional neural networks. In Proceedings of the International Conference on Neural Information Processing, pp. 569-578.

Yang, J., Lei, Z., Yi, D., & Li, S. Z. (2015). Person-specific face antispoofing with subject domain adaptation. IEEE Transactions on Information Forensics and Security, 10(4), 797-809. https://doi.org/10.1109/TIFS.2015.2403306