Deepfakes y autenticación biométrica: desafíos y soluciones contra la suplantación de identidad
DOI:
https://doi.org/10.64424/rcu51202698Palabras clave:
Aprendizaje automático, Aprendizaje multimodal, Blockchain, Ciberseguridad, Detección de fraudes, Inteligencia artificial generativaResumen
Los deepfakes en base a la inteligencia artificial generativa representan un desafío crítico en la autenticación biométrica, debido a que permite la creación de identidades sintéticas complicadas de detectar para los sistemas y el ojo humano. El objetivo principal es analizar los desafíos de la autenticación biométrica frente a deepfakes y las soluciones que existen para evitar la suplantación de identidad. Se realizó una revisión sistemática bajo la metodología PRISMA, empleando bases de datos como Google Académico, IEEE Xplore, ScienceDirect, Springer Link y Core, a partir de cadenas de búsqueda relacionadas con deepfake, biometric authentication y identity spoofing. De un total de 18432 resultados iniciales se seleccionaron 23 artículos publicados entre 2020 y 2025. Los hallazgos evidencian que los deepfakes afectan tanto al reconocimiento facial como de voz, generando vulnerabilidades frente a ataques sofisticados que superan técnicas tradicionales de detección. Entre los principales desafíos identificados se encuentran la dependencia de datasets reducidos y la falta de generalización de los modelos en contextos reales. Las soluciones revisadas incluyen enfoques multimodales, detección basada en biometría conductual, integración de blockchain, autenticación multifactor y modelos de aprendizaje continuo, optimizados para tiempo real. Se resalta que existen implicaciones éticas y legales que requieren marcos regulatorios internacionales y códigos de conducta para desarrolladores. Por ende, la seguridad biométrica frente a deepfakes demanda un enfoque integral que combine innovación tecnológica, regulación y educación digital, incentivando futuras investigaciones orientadas a datasets diversos y algoritmos adaptativos
Citas
Alanazi, F., Ushaw, G., & Morgan, G. (2024). Improving Detection of DeepFakes through Facial Region Analysis in Images. Electronics, 13(1), 126. https://doi.org/10.3390/electronics13010126
Alazwari, S., Alsamri, M. O. J., Alamgeer, M., Alabdan, R., Alzahrani, I., Rizwanullah, M., & Osman, A. E. (2024). Artificial rabbits optimization with transfer learning based deepfake detection model for biometric applications. Ain Shams Engineering Journal, 15(12), 103057. https://doi.org/10.1016/j.asej.2024.103057.
Anton Firc, Kamil Malinka, Petr Hanáček, Deepfakes as a threat to a speaker and facial recognition: An overview of tools and attack vectors, Heliyon, Volume 9, Issue 4, 2023, e15090, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2023.e15090.
Bodepudi, A., & Reddy, M. (2020). Spoofing attacks and mitigation strategies in biometrics-as-a-service systems. Eigenpub Review of Science and Technology, 4(1), 1-14. https://www.researchgate.net/publication/372134385_Spoofing_Attacks_and_Mitigation_Strategies_in_Biometrics-as-a-Service_Systems
Biometrics. IOSR Journal of Computer Engineering. 25. 26-37. https://www.iosrjournals.org/iosr-jce/papers/Vol25-issue3/Ser-3/D2503032637.pdf
Dudykevych, V., Yevseiev, S., Mykytyn, H., Ruda, K., & Hulak, H. (2024). Detecting Deepfake modifications of biometric images using neural networks. Cybersecurity Providing in Information and Telecommunication Systems 2024, 3654, 391-397. https://elibrary.kubg.edu.ua/id/eprint/48581
Felipe Romero-Moreno, Deepfake detection in generative AI: A legal framework proposal to protect human rights, Computer Law & Security Review, Volume 58, 2025, 106162, ISSN 2212-473X, https://doi.org/10.1016/j.clsr.2025.106162.
Felouat, H., Nguyen, H. H., Yamagishi, J., & Echizen, I. (2025). 3DDGD: 3D Deepfake Generation and Detection Using 3D Face Meshes. IEEE access: practical innovations, open solutions, 13, 107429–107441. https://doi.org/10.1109/access.2025.3580950
Firc, A., Malinka, K., & Hanáček, P. (2023). Deepfakes as a threat to a speaker and facial recognition: An overview of tools and attack vectors. Heliyon, 9(4), e15090. https://doi.org/10.1016/j.heliyon.2023.e15090
Gupta, P., Patil, H.A. & Guido, R.C. Vulnerability issues in Automatic Speaker Verification (ASV) systems. J AUDIO SPEECH MUSIC PROC. 2024, 10 (2024). https://doi.org/10.1186/s13636-024-00328-8
He, S., Lei, Y., Zhang, Z., Sun, Y., Li, S., Zhang, C., & Ye, J. (2025). Identity deepfake threats to biometric authentication systems: Public and expert perspectives. En arXiv [cs.HC]. http://arxiv.org/abs/2506.06825
Jbara, W. A., Hussein, N. A.-H. K., & Soud, J. H. (2024). Deepfake detection in video and audio clips: A comprehensive survey and analysis. Mesopotamian Journal of CyberSecurity, 4(3), 233–250. https://doi.org/10.58496/mjcs/2024/025
Khan, A., & Malik, K. M. (2023, December). Securing voice biometrics: One-shot learning approach for audio deepfake detection. In 2023 IEEE International Workshop on Information Forensics and Security (WIFS) (pp. 1-6). IEEE. https://doi.org/10.48550/arXiv.2310.03856
Khan, A.A., Laghari, A.A., Inam, S.A. et al. A survey on multimedia-enabled deepfake detection: state-of-the-art tools and techniques, emerging trends, current challenges & limitations, and future directions. Discov Computing 28, 48 (2025). https://doi.org/10.1007/s10791-025-09550-0
Krity Duhan, Abhishek Kajal, A Comparative Analysis of Deep Learning Based Approaches for DeepFake Identification, Procedia Computer Science, Volume 259, 2025, Pages 482-493, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2025.03.350.
Mandal, Sourav, Deep Fake Technology and Identity Theft: An Emerging Challenge for Cyber Laws in India (January 16, 2025). Available at SSRN: https://ssrn.com/abstract=5161545 or http://dx.doi.org/10.2139/ssrn.5161545
Mansoor, S. I. U. (2024). Legal implications of deepfake technology: In the context of manipulation, privacy, and identity theft. Central University of Kashmir Law Review, 4, 65-92. https://www.researchgate.net/publication/387499036_Legal_Implications_of_Deepfake_Technology_In_the_Context_of_Manipulation_Privacy_and_Identity_Theft
Min-Jen, T., & Cheng-Tao, C. (2024). Convolutional Neural Network for Detecting Deepfake Palmprint Images. IEEE access: practical innovations, open solutions, 12, 103405–103418. https://doi.org/10.1109/access.2024.3433497
Murillo Fonseca, N. (2025). Redefining identity and security: Spoofing in the age of artificial intelligence. https://repositorio.uniandes.edu.co/server/api/core/bitstreams/408c83e6-a08a-47b7-b32a-0ff6cc79db5e/content
Norman, J. D., & Farid, H. (2025). Detecting Deepfake Talking Heads from Facial Biometric Anomalies. arXiv preprint arXiv:2507.08917. https://doi.org/10.48550/arXiv.2507.08917
Page M J, McKenzie J E, Bossuyt P M, Boutron I, Hoffmann T C, Mulrow C D et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews BMJ 2021; 372 :n71 doi: https://doi.org/10.1136/bmj.n71
Pakina, Anil Kumar & Kumar, Dk & Goel, Anshul & Pujari, Tejaskumar. (2023). AI-Generated Synthetic Identities in Fin Tech: Detecting Deep fakes KYC Fraud Using Behavioral- https://www.researchgate.net/publication/390947564_AI-Generated_Synthetic_Identities_in_Fin_Tech_Detecting_Deep_fakes_KYC_Fraud_Using_Behavioral_Biometrics
Pawelec, M. Decent deepfakes? Professional deepfake developers’ ethical considerations and their governance potential. AI Ethics 5, 2641–2666 (2025). https://doi.org/10.1007/s43681-024-00542-2
Qureshi, S. M., Saeed, A., Almotiri, S. H., Ahmad, F., & Al Ghamdi, M. A. (2024). Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media. PeerJ. Computer Science, 10(e2037), e2037. https://doi.org/10.7717/peerj-cs.2037
Rabbi, B. M., Haque, E., Huri, H. S., & Tabassum, N. (2024). From impersonation to authentication: techniques for identifying deep fake voices (Doctoral dissertation, Brac University). http://hdl.handle.net/10361/25249
Salko, M., Firc, A., & Malinka, K. (2024, April). Security implications of deepfakes in face authentication. In Proceedings of the 39th ACM/SIGAPP symposium on applied computing (pp. 1376-1384). https://doi.org/10.1145/3605098.3635953
Šandor, O., & Malinka, M. K. (2023). Resilience of Biometric Authentication of Voice Assistants against Deepfakes. https://theses.cz/id/nr5tm7/OS_BP_FINAL_Archive.pdf
Singh, L.H., Charanarur, P. & Chaudhary, N.K. Advancements in detecting Deepfakes: AI algorithms and future prospects − a review. Discov Internet Things 5, 53 (2025). https://doi.org/10.1007/s43926-025-00154-0
Srivastava, A., Badal, T., Agarwal, M., Dwivedi, K., & Naik, A. H. (2025). Enhancing deepfake detection accuracy with vision transformers: A generative AI approach. En Communications in Computer and Information Science (pp. 68–78). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-84602-1_5
Usman, S. O. (2025). IMPACT OF DEEPFAKE TECHNOLOGY ON TELECOM SECURITY AND USER TRUST. https://www.researchgate.net/publication/395043194_IMPACT_OF_DEEPFAKE_TECHNOLOGY_ON_TELECOM_SECURITY_AND_USER_TRUST
Xing, H., Tan, S. Y., Qamar, F., & Jiao, Y. (2025). Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey. Applied Sciences, 15(12), 6891. https://doi.org/10.3390/app15126891
Zhang, C. J., Gill, A. Q., Liu, B., & Anwar, M. J. (2025). AI-based identity fraud detection: A systematic review. En arXiv [cs.AI]. http://arxiv.org/abs/2501.09239
Publicado
Número
Sección
Licencia
Derechos de autor 2026 Deysi Elvia Yuvixa Quiliche Plasencia, Kelita Marilu Mauricio Saavedra, Alberto Carlos Mendoza de los Santos
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
© 2022