Redefining identity and security: Spoofing in the age of artificial intelligence

This research explores the growing threat of AI-driven deepfakes in the context of spoofing attacks. By systematically analyzing attack models, including facial reenactment, replacement, editing, and synthesis, as well as audio deepfake techniques, the study dissects both the creation mechanisms and...

Full description

Autores:
Murillo Fonseca, Nicole
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2025
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/76116
Acceso en línea:
https://hdl.handle.net/1992/76116
Palabra clave:
Spoofing
Deepfake
Security
Detection
Authentication
Artificial intelligence
Ingeniería
Rights
openAccess
License
Attribution-NonCommercial 4.0 International
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repository_id_str
dc.title.eng.fl_str_mv Redefining identity and security: Spoofing in the age of artificial intelligence
title Redefining identity and security: Spoofing in the age of artificial intelligence
spellingShingle Redefining identity and security: Spoofing in the age of artificial intelligence
Spoofing
Deepfake
Security
Detection
Authentication
Artificial intelligence
Ingeniería
title_short Redefining identity and security: Spoofing in the age of artificial intelligence
title_full Redefining identity and security: Spoofing in the age of artificial intelligence
title_fullStr Redefining identity and security: Spoofing in the age of artificial intelligence
title_full_unstemmed Redefining identity and security: Spoofing in the age of artificial intelligence
title_sort Redefining identity and security: Spoofing in the age of artificial intelligence
dc.creator.fl_str_mv Murillo Fonseca, Nicole
dc.contributor.advisor.none.fl_str_mv Donoso Meisel, Yezyd Enrique
dc.contributor.author.none.fl_str_mv Murillo Fonseca, Nicole
dc.subject.keyword.eng.fl_str_mv Spoofing
Deepfake
Security
Detection
Authentication
Artificial intelligence
topic Spoofing
Deepfake
Security
Detection
Authentication
Artificial intelligence
Ingeniería
dc.subject.themes.none.fl_str_mv Ingeniería
description This research explores the growing threat of AI-driven deepfakes in the context of spoofing attacks. By systematically analyzing attack models, including facial reenactment, replacement, editing, and synthesis, as well as audio deepfake techniques, the study dissects both the creation mechanisms and detection methodologies underlying these sophisticated forms of edited media content. Leveraging insights from neural network architectures such as GANs, encoder-decoder models, and recurrent networks, alongside a comprehensive review of detection strategies ranging from motion and texture analysis to sensor-based and end-to-end deep learning approaches, the work provides a holistic perspective on the vulnerabilities inherent in current systems and underscores the urgency of developing more robust, adaptable security measures, as well as, clear policies and regulations regarding the use and availability of artificial intelligence tools.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-03-04T19:09:58Z
dc.date.available.none.fl_str_mv 2025-03-04T19:09:58Z
dc.date.issued.none.fl_str_mv 2025-02-05
dc.type.none.fl_str_mv Trabajo de grado - Pregrado
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.type.content.none.fl_str_mv Text
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url https://hdl.handle.net/1992/76116
identifier_str_mv instname:Universidad de los Andes
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repourl:https://repositorio.uniandes.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.none.fl_str_mv M. Salko, A. Firc, and K. Malinka, “Security Implications of Deepfakes in Face Authentication,” Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. ACM, pp. 1376–1384, 2024. doi: 10.1145/3605098.3635953.
Y. Mirsky and W. Lee, “The Creation and Detection of Deepfakes,” ACM Computing Surveys, vol. 54, no. 1. Association for Computing Machinery (ACM), pp. 1–41, 2021. doi: 10.1145/3425780.
M. Borak, “Chinese government-run facial recognition system hacked by tax fraudsters: report,” 2021. Available: https://www.scmp.com/tech/tech-trends/article/3127645/chinese-government-run-facial-recognition-system-hacked-tax (accessed Nov. 03, 2024) [Online].
D. Prudký, A. Firc, and K. Malinka, “Assessing the Human Ability to Recognize Synthetic Speech in Ordinary Conversation,” 2023 International Conference of the Biometrics Special Interest Group (BIOSIG). IEEE, pp. 1–5, 2023. doi: 10.1109/biosig58226.2023.10346006.
Y. Li, X. Yang, P. Sun, H. Qi and S. Lyu, "Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 3204-3213, doi: 10.1109/CVPR42600.2020.00327.
K. Liu, I. Perov, D. Gao, N. Chervoniy, W. Zhou, and W. Zhang, “Deepfacelab: Integrated, flexible and extensible face-swapping framework,” Pattern Recognition, vol. 141. Elsevier BV, p. 109628, Sep. 2023. doi: 10.1016/j.patcog.2023.109628.
Z. Almutairi and H. Elgibreen, “A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions,” Algorithms, vol. 15, no. 5. MDPI AG, p. 155, 2022. doi: 10.3390/a15050155.
Y. L. Khaleel, M. A. Habeeb, and H. Alnabulsi, “Adversarial Attacks in Machine Learning: Key Insights and Defense Approaches,” Applied Data Science and Analysis, vol. 2024. Mesopotamian Academic Press, pp. 121–147, 2024. doi: 10.58496/adsa/2024/011.
S. H. Silva, M. Bethany, A. M. Votto, I. H. Scarff, N. Beebe, and P. Najafirad, “Deepfake forensics analysis: An explainable hierarchical ensemble of weakly supervised models,” Forensic Science International: Synergy, vol. 4. Elsevier BV, p. 100217, 2022. doi: 10.1016/j.fsisyn.2022.100217.
Z. Almutairi and H. Elgibreen, “A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions,” Algorithms, vol. 15, no. 5. MDPI AG, p. 155, 2022. doi: 10.3390/a15050155.
K. Schäfer, J.-E. Choi, and S. Zmudzinski, “Explore the World of Audio Deepfakes: A Guide to Detection Techniques for Non-Experts,” 3rd ACM International Workshop on Multimedia AI against Disinformation. ACM, pp. 13–22, 2024. doi: 10.1145/3643491.3660289.
J.E. Choi, K. Schäfer, and S. Zmudzinski, “Introduction to Audio Deepfake Generation: Academic Insights for Non-Experts,” 3rd ACM International Workshop on Multimedia AI against Disinformation. ACM, pp. 3–12, 2024. doi: 10.1145/3643491.3660286.
P. Isola, J.-Y. Zhu, T. Zhou and A. A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 5967-5976, doi: 10.1109/CVPR.2017.632.
S. R. Akhdan, R. Supriyanti, and A. S. Nugroho, “Face recognition with anti spoofing eye blink detection,” AIP Conference Proceedings, vol. 2562. AIP Publishing, p. 020006, 2023. doi: 10.1063/5.0113512.
K. Kollreider, H. Fronthaler, M. I. Faraj and J. Bigun, "Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment," in IEEE Transactions on Information Forensics and Security, vol. 2, no. 3, pp. 548-558, 2007, doi: 10.1109/TIFS.2007.902037.
S. Concas, S. M. La Cava, R. Casula, G. Orrù, G. Puglisi, and G. L. Marcialis, “Quality-based Artifact Modeling for Facial Deepfake Detection in Videos,” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, pp. 3845–3854, 2024. doi: 10.1109/cvprw63382.2024.00389.
M. R. Hasan, S. M. Hasan Mahmud, and X. Y. Li, “Face Anti-Spoofing Using Texture-Based Techniques and Filtering Methods,” Journal of Physics: Conference Series, vol. 1229, no. 1. IOP Publishing, p. 012044, 2019. doi: 10.1088/1742-6596/1229/1/012044.
B. Xu, J. Liu, J. Liang, W. Lu, and Y. Zhang, “DeepFake Videos Detection Based on Texture Features,” Computers, Materials & Continua, vol. 68, no. 1. Tech Science Press, pp. 1375–1388, 2021. doi: 10.32604/cmc.2021.016760.
S.-H. Kim, S.-M. Jeon, and E. C. Lee, “Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal,” Sensors, vol. 22, no. 8. MDPI AG, p. 3070, 2022. doi: 10.3390/s22083070.
G. Manoj, D. S. Yashas, K. P. Jeevan, M. Likith and Dr. Raghavendra R. J. “A Survey on Anti-Spoofing Methods for Facial Recognition,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology. Technoscience Academy, pp. 259–268, Apr. 15, 2022. doi: 10.32628/cseit228248.
A. Neema, S. K. Sharma, and D. Sukheja, “Machine Learning Method for Face Spoofing: A Review,” Journal of Propulsion Technology , vol. 44, no. 4, 2023, (accessed Dec. 27, 2024) [Online]. Available: https://www.propulsiontechjournal.com/index.php/journal/article/view/5452/5531
Y. Atoum, Y. Liu, A. Jourabloo and X. Liu, "Face anti-spoofing using patch and depth-based CNNs," 2017 IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA, 2017, pp. 319-328, doi: 10.1109/BTAS.2017.8272713.
L. Wu, Y. Xu, M. Jian, W. Cai, C. Yan, and Y. Ma, “Motion Analysis Based Cross-Database Voting for Face Spoofing Detection,” Lecture Notes in Computer Science. Springer International Publishing, pp. 528–536, 2017. doi: 10.1007/978-3-319-69923-3_57.
J. Xiao, W. Wang, L. Zhang, and H. Liu, “A Mobile FaceNet-Based Face Anti-Spoofing Algorithm for Low-Quality Images,” Electronics, vol. 13, no. 14. MDPI AG, p. 2801, 2024. doi: 10.3390/electronics13142801.
H. Hao, M. Pei, and M. Zhao, “Face Liveness Detection Based on Client Identity Using Siamese Network,” Lecture Notes in Computer Science. Springer International Publishing, pp. 172–180, 2019. doi: 10.1007/978-3-030-31654-9_15.
G. Hua, A. B. J. Teoh, and H. Zhang, “Towards End-to-End Synthetic Speech Detection,” IEEE Signal Processing Letters, vol. 28. Institute of Electrical and Electronics Engineers (IEEE), pp. 1265–1269, 2021. doi: 10.1109/lsp.2021.3089437.
T. J. Hong, “Uncovering the Real Voice: How to Detect and Verify Audio Deepfakes,” Medium, 2023. https://medium.com/htx-s-s-coe/uncovering-the-real-voice-how-to-detect-and-verify-audio-deepfakes-42e480d3f431 (accessed Jan. 04, 2024) [Online].
J. Khochare, C. Joshi, B. Yenarkar, S. Suratkar, and F. Kazi, “A Deep Learning Framework for Audio Deepfake Detection,” Arabian Journal for Science and Engineering, vol. 47, no. 3. Springer Science and Business Media LLC, pp. 3447–3458, 2021. doi: 10.1007/s13369-021-06297-w.
F. Iqbal, A. Abbasi, A. R. Javed, Z. Jalil, and J. N. Al-Karaki, "Deepfake Audio Detection Via Feature Engineering And Machine Learning," in Proc. CIKM Workshops, 2022.
P. Rahul, P. Aravind, C. Ranjith, U. Nechiyil, and N. Paramparambath, “Audio Spoofing Verification using Deep Convolutional Neural Networks by Transfer Learning,” 2020, arXiv. doi: 10.48550/ARXIV.2008.03464.
C. B. Tan, M. H. Ahmad Hijazi, F. Kok, M. S. Mohamad, and P. N. Ellyza Nohuddin, “Artificial speech detection using image-based features and random forest classifier,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 11, no. 1. Institute of Advanced Engineering and Science, p. 161, 2022. doi: 10.11591/ijai.v11.i1.pp161-172.
C. Wang, J. Yi, J. Tao, H. Sun, X. Chen, Z. Tian, H. Ma, C. Fan, and R. Fu, “Fully Automated End-to-End Fake Audio Detection,” Proceedings of the 1st International Workshop on Deepfake Detection for Audio Multimedia. ACM, pp. 27–33, 2022. doi: 10.1145/3552466.3556530.
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spelling Donoso Meisel, Yezyd Enriquevirtual::24001-1Murillo Fonseca, Nicole2025-03-04T19:09:58Z2025-03-04T19:09:58Z2025-02-05https://hdl.handle.net/1992/76116instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/This research explores the growing threat of AI-driven deepfakes in the context of spoofing attacks. By systematically analyzing attack models, including facial reenactment, replacement, editing, and synthesis, as well as audio deepfake techniques, the study dissects both the creation mechanisms and detection methodologies underlying these sophisticated forms of edited media content. Leveraging insights from neural network architectures such as GANs, encoder-decoder models, and recurrent networks, alongside a comprehensive review of detection strategies ranging from motion and texture analysis to sensor-based and end-to-end deep learning approaches, the work provides a holistic perspective on the vulnerabilities inherent in current systems and underscores the urgency of developing more robust, adaptable security measures, as well as, clear policies and regulations regarding the use and availability of artificial intelligence tools.Pregrado30 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería de Sistemas y ComputaciónAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Redefining identity and security: Spoofing in the age of artificial intelligenceTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPSpoofingDeepfakeSecurityDetectionAuthenticationArtificial intelligenceIngenieríaM. Salko, A. Firc, and K. Malinka, “Security Implications of Deepfakes in Face Authentication,” Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. ACM, pp. 1376–1384, 2024. doi: 10.1145/3605098.3635953.Y. Mirsky and W. Lee, “The Creation and Detection of Deepfakes,” ACM Computing Surveys, vol. 54, no. 1. Association for Computing Machinery (ACM), pp. 1–41, 2021. doi: 10.1145/3425780.M. Borak, “Chinese government-run facial recognition system hacked by tax fraudsters: report,” 2021. Available: https://www.scmp.com/tech/tech-trends/article/3127645/chinese-government-run-facial-recognition-system-hacked-tax (accessed Nov. 03, 2024) [Online].D. Prudký, A. Firc, and K. Malinka, “Assessing the Human Ability to Recognize Synthetic Speech in Ordinary Conversation,” 2023 International Conference of the Biometrics Special Interest Group (BIOSIG). IEEE, pp. 1–5, 2023. doi: 10.1109/biosig58226.2023.10346006.Y. Li, X. Yang, P. Sun, H. Qi and S. Lyu, "Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 3204-3213, doi: 10.1109/CVPR42600.2020.00327.K. Liu, I. Perov, D. Gao, N. Chervoniy, W. Zhou, and W. Zhang, “Deepfacelab: Integrated, flexible and extensible face-swapping framework,” Pattern Recognition, vol. 141. Elsevier BV, p. 109628, Sep. 2023. doi: 10.1016/j.patcog.2023.109628.Z. Almutairi and H. Elgibreen, “A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions,” Algorithms, vol. 15, no. 5. MDPI AG, p. 155, 2022. doi: 10.3390/a15050155.Y. L. Khaleel, M. A. Habeeb, and H. Alnabulsi, “Adversarial Attacks in Machine Learning: Key Insights and Defense Approaches,” Applied Data Science and Analysis, vol. 2024. Mesopotamian Academic Press, pp. 121–147, 2024. doi: 10.58496/adsa/2024/011.S. H. Silva, M. Bethany, A. M. Votto, I. H. Scarff, N. Beebe, and P. Najafirad, “Deepfake forensics analysis: An explainable hierarchical ensemble of weakly supervised models,” Forensic Science International: Synergy, vol. 4. Elsevier BV, p. 100217, 2022. doi: 10.1016/j.fsisyn.2022.100217.Z. Almutairi and H. Elgibreen, “A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions,” Algorithms, vol. 15, no. 5. MDPI AG, p. 155, 2022. doi: 10.3390/a15050155.K. Schäfer, J.-E. Choi, and S. Zmudzinski, “Explore the World of Audio Deepfakes: A Guide to Detection Techniques for Non-Experts,” 3rd ACM International Workshop on Multimedia AI against Disinformation. ACM, pp. 13–22, 2024. doi: 10.1145/3643491.3660289.J.E. Choi, K. Schäfer, and S. Zmudzinski, “Introduction to Audio Deepfake Generation: Academic Insights for Non-Experts,” 3rd ACM International Workshop on Multimedia AI against Disinformation. ACM, pp. 3–12, 2024. doi: 10.1145/3643491.3660286.P. Isola, J.-Y. Zhu, T. Zhou and A. A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 5967-5976, doi: 10.1109/CVPR.2017.632.S. R. Akhdan, R. Supriyanti, and A. S. Nugroho, “Face recognition with anti spoofing eye blink detection,” AIP Conference Proceedings, vol. 2562. AIP Publishing, p. 020006, 2023. doi: 10.1063/5.0113512.K. Kollreider, H. Fronthaler, M. I. Faraj and J. Bigun, "Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment," in IEEE Transactions on Information Forensics and Security, vol. 2, no. 3, pp. 548-558, 2007, doi: 10.1109/TIFS.2007.902037.S. Concas, S. M. La Cava, R. Casula, G. Orrù, G. Puglisi, and G. L. Marcialis, “Quality-based Artifact Modeling for Facial Deepfake Detection in Videos,” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, pp. 3845–3854, 2024. doi: 10.1109/cvprw63382.2024.00389.M. R. Hasan, S. M. Hasan Mahmud, and X. Y. Li, “Face Anti-Spoofing Using Texture-Based Techniques and Filtering Methods,” Journal of Physics: Conference Series, vol. 1229, no. 1. IOP Publishing, p. 012044, 2019. doi: 10.1088/1742-6596/1229/1/012044.B. Xu, J. Liu, J. Liang, W. Lu, and Y. Zhang, “DeepFake Videos Detection Based on Texture Features,” Computers, Materials & Continua, vol. 68, no. 1. Tech Science Press, pp. 1375–1388, 2021. doi: 10.32604/cmc.2021.016760.S.-H. Kim, S.-M. Jeon, and E. C. Lee, “Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal,” Sensors, vol. 22, no. 8. MDPI AG, p. 3070, 2022. doi: 10.3390/s22083070.G. Manoj, D. S. Yashas, K. P. Jeevan, M. Likith and Dr. Raghavendra R. J. “A Survey on Anti-Spoofing Methods for Facial Recognition,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology. Technoscience Academy, pp. 259–268, Apr. 15, 2022. doi: 10.32628/cseit228248.A. Neema, S. K. Sharma, and D. Sukheja, “Machine Learning Method for Face Spoofing: A Review,” Journal of Propulsion Technology , vol. 44, no. 4, 2023, (accessed Dec. 27, 2024) [Online]. Available: https://www.propulsiontechjournal.com/index.php/journal/article/view/5452/5531Y. Atoum, Y. Liu, A. Jourabloo and X. Liu, "Face anti-spoofing using patch and depth-based CNNs," 2017 IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA, 2017, pp. 319-328, doi: 10.1109/BTAS.2017.8272713.L. Wu, Y. Xu, M. Jian, W. Cai, C. Yan, and Y. Ma, “Motion Analysis Based Cross-Database Voting for Face Spoofing Detection,” Lecture Notes in Computer Science. Springer International Publishing, pp. 528–536, 2017. doi: 10.1007/978-3-319-69923-3_57.J. Xiao, W. Wang, L. Zhang, and H. Liu, “A Mobile FaceNet-Based Face Anti-Spoofing Algorithm for Low-Quality Images,” Electronics, vol. 13, no. 14. MDPI AG, p. 2801, 2024. doi: 10.3390/electronics13142801.H. Hao, M. Pei, and M. Zhao, “Face Liveness Detection Based on Client Identity Using Siamese Network,” Lecture Notes in Computer Science. Springer International Publishing, pp. 172–180, 2019. doi: 10.1007/978-3-030-31654-9_15.G. Hua, A. B. J. Teoh, and H. Zhang, “Towards End-to-End Synthetic Speech Detection,” IEEE Signal Processing Letters, vol. 28. Institute of Electrical and Electronics Engineers (IEEE), pp. 1265–1269, 2021. doi: 10.1109/lsp.2021.3089437.T. J. Hong, “Uncovering the Real Voice: How to Detect and Verify Audio Deepfakes,” Medium, 2023. https://medium.com/htx-s-s-coe/uncovering-the-real-voice-how-to-detect-and-verify-audio-deepfakes-42e480d3f431 (accessed Jan. 04, 2024) [Online].J. Khochare, C. Joshi, B. Yenarkar, S. Suratkar, and F. Kazi, “A Deep Learning Framework for Audio Deepfake Detection,” Arabian Journal for Science and Engineering, vol. 47, no. 3. Springer Science and Business Media LLC, pp. 3447–3458, 2021. doi: 10.1007/s13369-021-06297-w.F. Iqbal, A. Abbasi, A. R. Javed, Z. 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