Deep Spoof Face Detection Techniques in React Native
DOI:
https://doi.org/10.29304/jqcsm.2025.17.42582Keywords:
Face Detection Using Deep Learning,Abstract
The rapid rise in the use of artificially generated faces has significantly increased the risk of identity theft in biometric authentication systems. Modern facial recognition technologies are now vulnerable to sophisticated attacks using printed images, replayed videos, and highly realistic 3D masks. This creates an urgent need for advanced, reliable, and mobile-compatible fake face detection systems. Research indicates that while deep learning models have demonstrated strong performance in detecting artificially generated faces, deploying these models on consumer mobile devices remains challenging due to limitations in computing power, memory, privacy, and processing speed. This paper highlights several key challenges: (1) optimizing deep learning models to operate efficiently on mobile devices, (2) ensuring real-time inference without compromising accuracy, (3) maintaining user privacy when processing sensitive facial data, and (4) addressing the variability in mobile phone cameras, input resolution, and platform limitations across Android and iOS. Furthermore, the increasing sophistication of identity spoofing attacks—such as 3D masks and AI-generated faces—demands more sophisticated, robust, and multimodal detection technologies. The research findings provide a clear roadmap toward practical solutions. By evaluating the latest deep learning architectures, datasets, and anti-spoofing metrics, the study proposes a comprehensive React Native deployment path using TensorFlow Lite and TensorFlow.js to ensure cross-platform compatibility. The proposed system offers a unified classification of identity spoofing attacks and defense mechanisms, along with a structured evaluation framework that compares on-device processing with server-side detection. The results demonstrate that optimized models can achieve high accuracy, low false accept/rejection rates, and sub-second processing speeds on mobile devices. Ultimately, the study provides practical design guidelines for building robust, privacy-preserving, efficient, and real-world consumer-grade fake face detection systems.
Downloads
References
M. Fang et al., “Face Anti-Spoofing for Biometric Security Systems,” Applied Sciences, vol. 15, no. 12, p. 6891, (2024).
M. Ferrara, A. Franco, and D. Maltoni, “On the Vulnerability of Face Recognition Systems to Deep Morphing Attacks,” arXiv preprint arXiv:1910.01933, (2019).
Z. Xiong, Y. Yang, and X. Zhang, “A Comprehensive Survey on Face Anti-Spoofing Techniques,” Synthesis Lectures on Image, Video, and Multimedia Processing, Now Publishers, (2024).
L. Li, X. Wang, and G. Hua, “3D Mask Presentation Attack Detection: A Survey,” arXiv preprint arXiv:2003.03151, (2020).
S. F. Ahmed, M. S. B. Alam, M. Hassan, and M. R. Rozbu, “Deep learning modelling techniques: current progress, applications, advantages, and challenges,” Artificial Intelligence, Springer, (2023).
G. Botelho de Souza, J. P. Papa, and A. N. Marana, “On the Learning of Deep Local Features for Robust Face Spoofing Detection,” (2018).
M. Fang, N. Damer, F. Kirchbuchner, and A. Kuijper, “Real masks and spoof faces: On the masked face presentation attack detection,” Pattern Recognition, (2022).
M. Abdul-Al, G. K. Kyeremeh, R. Qahwaji, and N. T. Ali, “The Evolution of Biometric Authentication: A Deep Dive Into Multi-Modal Facial Recognition: A Review Case Study,” IEEE, (2024).
C. Nagpal and S. R. Dubey, “A Performance Evaluation of Convolutional Neural Networks for Face Anti-Spoofing,” (2018).
A. Goyal and Y. Bengio, “Inductive biases for deep learning of higher-level cognition,” Proc. Roy. Soc. A, (2022).
A. S. Qaddoori, J. H. Saud, and F. A. Hamad, “A classifier design for micro bubble generators based on deep learning technique,” Proceedings, vol. 80, no. 3, p. 1705, (2023).
S. Liqaa M and J. H. Saud, “Deep video understanding based on language generation,” Int. J. Cloud Comput. Database Manag., vol. 6, no. 1, pp. 9–15, (2025).
S. Liqaa M. and J. H. Saud, “Deep Learning and Fusion Techniques for High-Precision Image Matting,” Academia Open, vol. 10, no. 1, (2025).
M. Talib and J. H. Saud, “A Multi-Weapon Detection Using Deep Learning,” Iraqi J. Inf. Commun. Technol., vol. 7, no. 1, (Apr. 2024).
A. Malik, M. Kuribayashi, S. M. Abdullahi, and A. N. Khan, “DeepFake detection for human face images and videos: A survey,” IEEE Access, (2022).
A. Guesmi, M. A. Hanif, B. Ouni, and M. Shafique, “Physical adversarial attacks for camera-based smart systems: Current trends, categorization, applications, research challenges, and future outlook,” IEEE Access, (2023).
A. F. Ebihara, K. Sakurai, and H. Imaoka, “Efficient face spoofing detection with flash,” IEEE Trans., (2021).
Z. Yu, Y. Qin, X. Li, C. Zhao, and Z. Lei, “Deep learning for face anti-spoofing: A survey,” in Analysis and Machine, (2022).
D. Sharma and A. Selwal, “A survey on face presentation attack detection mechanisms: hitherto and future perspectives,” Multimedia Systems, Springer, (2023).
Z. Yu et al., “Deep Learning for Face Anti-Spoofing: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 5, pp. 5609–5631, (May 2023).
P.-K. Huang et al., “A Survey on Deep Learning-based Face Anti-Spoofing,” APSIPA Trans. Signal Inf. Process., vol. 13, no. 1, e34, (Dec. 2024).
H. Xing, S. Y. Tan, F. Qamar, and Y. Jiao, “Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey,” Applied Sciences, vol. 15, no. 12, p. 6891, (2025).
Z. Ming, M. M. Luqman, M. Visani, and J.-C. Burie, “A Survey On Anti-Spoofing Methods For Face Recognition With RGB Cameras Of Generic Consumer Devices,” J. Imaging, vol. 6, no. 12, 139, (Dec. 2020).
M. M. Luqman and J.-C. Burie, “A Survey on Anti-Spoofing Methods for Facial Recognition with RGB Cameras of Generic Consumer Devices,” arXiv preprint arXiv:2010.04145, (Oct. 2020).
Z. Yu et al., “Deep Learning for Face Anti-Spoofing: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 5, pp. 5609–5631, (May 2023).
P.-K. Huang et al., “A Survey on Deep Learning-based Face Anti-Spoofing,” APSIPA Trans. Signal Inf. Process., vol. 13, no. 1, e34, (Dec. 2024).
H. Xing et al., “Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey,” Applied Sciences, vol. 15, no. 12, p. 6891, (2025).
M. Zhang, K. Zeng, and J. Wang, “A Survey on Face Anti-Spoofing Algorithms,” J. Inf. Hiding Privacy Prot., (2020).
“A Survey on Anti-Spoofing Methods for Facial Recognition with RGB Cameras of Generic Consumer Devices,” (2022).
J. Xiao, W. Wang, L. Zhang, and H. Liu, “A MobileFaceNet-Based Face Anti-Spoofing Algorithm for Low-Quality Images,” Electronics, vol. 13, no. 14, art. 2801, (2024).
J. C. Bernal-Romero and J. M. Ramirez-Cortes, “A review on protection and cancelable techniques in biometric systems,” IEEE, (2023).
W. H. Abdulla and F. Marattukalam, “Exploring Human Biometrics: A Focus on Security Concerns and Deep Neural Networks,” APSIPA Trans., (2023).
K. Jha, S. Srivastava, and A. Jain, “A novel texture-based approach for facial liveness detection and authentication using deep learning classifier,” Int. J. …, (2024).
S. Policepatil and S. M. Hatture, “Face liveness detection: An overview,” J. Sci. Res. Sci., (2021).
T. Zohud and S. Zein, “Cross-platform mobile app development in industry: A multiple case-study,” Int. J. Computing, (2021).
S. S. Sarpotdar, “A Novel Face-Anti Spoofing Neural Network Model For Face Recognition And Detection,” (2022).
C. Xu and J. McAuley, “A survey on model compression and acceleration for pretrained language models,” in Proc. AAAI Conf. Artif. Intell., (2023).
R. Nagy, “Simplifying Application Development with Kotlin Multiplatform Mobile,” (2022).
S. Khan, P. H. Nguyen, and A. Abdul-Rahman, “Rapid development of a data visualization service in an emergency response,” IEEE Trans., (2022).
M. Taeb and H. Chi, “Comparison of deepfake detection techniques through deep learning,” J. Cybersecurity Privacy, (2022).
V. Sorrenti, “Image Classification in the Browser: A performance assessment,” (2023).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Saud, Jamila H., Shoobi, Liqaa M., Wurood A. Jbara, Reiam Abd Alkarsim Abd
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
