Deepfake Technology's Dual Nature: A Review of Security Risk Assessment and Defense Strategies
Research Article
Open Access
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Deepfake Technology's Dual Nature: A Review of Security Risk Assessment and Defense Strategies

Wufeiyang Chen 1*
1 Faculty of Science and Engineering (FoSE), University of Nottingham Ningbo China, Ningbo, Zhejiang, China, 315100
*Corresponding author: cwfeiyang@gmail.com
Published on 3 September 2025
Journal Cover
ACE Vol.183
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-341-3
ISBN (Online): 978-1-80590-342-0
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Abstract

Deepfake technology, empowered by breakthroughs in deep learning-based image synthesis, is profoundly reshaping identity verification systems, finding extensive application in security, finance, and social media with enhanced convenience. However, its capacity to generate hyper-realistic facial forgeries presents a dual impact: while driving innovation, it simultaneously introduces unprecedented security threats, including privacy violations, identity spoofing, and data poisoning attacks. This paper systematically reviews and assesses current research progress on the security risks and defense strategies associated with Deepfake technology. Through synthesis of existing literature, this paper constructs a multidimensional analytical framework examining three core dimensions: first, the core technological principles underpinning Deepfakes and their evolution; second, the diverse spectrum of security risks arising from their misuse and their underlying mechanisms; and third, the effectiveness and inherent limitations of prevailing defense mechanisms, encompassing detection techniques and legal regulations. This study concludes that although Deepfakes advance facial recognition, mitigating their inherent security threats necessitates a multidimensional synergistic approach. This approach must integrate continuous technological advancements, robust legal oversight, and strengthened public awareness initiatives. Future efforts must prioritize establishing cross-disciplinary collaborative governance mechanisms to achieve a dynamic equilibrium between technological innovation and security assurance.

Keywords:

Deepfake, Deepfake Detection, Generative Adversarial Networks, Deep Learning, Fake News

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Chen,W. (2025). Deepfake Technology's Dual Nature: A Review of Security Risk Assessment and Defense Strategies. Applied and Computational Engineering,183,14-20.

References

[1]. Masood M., Nawaz M., Malik K.M., et al. (2023) Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward. Appl Intell 53(4): 3974–4026

[2]. Liu M.Y., Huang X., Yu J., et al. (2021b) Generative adversarial networks for image and video synthesis: algorithms and applications. Proc IEEE, 109(5): 839–862.

[3]. Khan, R., Sohail, M., Usman, I., Sandhu, M., Raza, M., Yaqub, M. A., & Liotta, A. (2024). Comparative study of deep learning techniques for DeepFake video detection. ICT Express, 10(6), 1226-1239.

[4]. Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024). Deepfake video detection: challenges and opportunities. Artificial Intelligence Review, 57(6): 159.

[5]. Mubarak R., Alsboui T., Alshaikh O., et al. (2023) A survey on the detection and impacts of deepfakes in visual, audio, and textual formats. IEEE Access 11: 144497–14452. https: //doi.org/10.1109/ ACCESS.2023.3344653

[6]. Guardian. (2023) Tom Hanks says AI version of him used in dental plan ad. https: //www.theguardian.com/film/2023/oct/02/tom-hanks-dental-ad-ai-version-fake.

[7]. Isenstadt A. (2023) DeSantis PAC uses AI-generated Trump. Politico. https: //www.politico.com/news/2023/07/17/desantis-pac-ai-generated-trump-in-ad-00106695.

[8]. Stanley-Becker I., and Nix N. (2023) Fake images of Trump arrest...Washington Post (March 22). https: //www.washingtonpost.com/politics/2023/03/22/trump-arrest-deepfakes/.

[9]. Binder M. (2023) Republicans release AI-generated attack ad. Mashable, April 25 https: //mashable.com/video/republican-attack-ad-biden-reelection-ai.

[10]. Zhou, X., Wang, Y., & Wu, P. (2020). Detecting deepfake videos via frame serialization learning. Paper presented at the 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI).

[11]. Heidari, A., Jafari Navimipour, N., Dag, H., & Unal, M. (2024). Deepfake detection using deep learning methods: A systematic and comprehensive review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(2), e1520.

[12]. Zhou, Y., & Lim, S. N. (2021). Joint audio-visual deepfake detection. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 14800-14809.

[13]. Oorloff, T., Koppisetti, S., Bonettini, N., Solanki, D., Colman, B., Yacoob, Y., ... & Bharaj, G. (2024). Avff: Audio-visual feature fusion for video deepfake detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 27102-27112.

[14]. Ilyas, H., Javed, A., & Malik, K. M. (2023). AVFakeNet: A unified end-to-end Dense Swin Transformer deep learning model for audio–visual​ deepfakes detection. Applied Soft Computing, 136, 110124.

[15]. Roundtree, A. K. (2025). Deepfake Laws Protecting Childhood Safety and Future: An NLP Analysis. In Proceedings of the 24th Interaction Design and Children, pp. 1086-1090.

References

[1]. Masood M., Nawaz M., Malik K.M., et al. (2023) Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward. Appl Intell 53(4): 3974–4026

[2]. Liu M.Y., Huang X., Yu J., et al. (2021b) Generative adversarial networks for image and video synthesis: algorithms and applications. Proc IEEE, 109(5): 839–862.

[3]. Khan, R., Sohail, M., Usman, I., Sandhu, M., Raza, M., Yaqub, M. A., & Liotta, A. (2024). Comparative study of deep learning techniques for DeepFake video detection. ICT Express, 10(6), 1226-1239.

[4]. Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024). Deepfake video detection: challenges and opportunities. Artificial Intelligence Review, 57(6): 159.

[5]. Mubarak R., Alsboui T., Alshaikh O., et al. (2023) A survey on the detection and impacts of deepfakes in visual, audio, and textual formats. IEEE Access 11: 144497–14452. https: //doi.org/10.1109/ ACCESS.2023.3344653

[6]. Guardian. (2023) Tom Hanks says AI version of him used in dental plan ad. https: //www.theguardian.com/film/2023/oct/02/tom-hanks-dental-ad-ai-version-fake.

[7]. Isenstadt A. (2023) DeSantis PAC uses AI-generated Trump. Politico. https: //www.politico.com/news/2023/07/17/desantis-pac-ai-generated-trump-in-ad-00106695.

[8]. Stanley-Becker I., and Nix N. (2023) Fake images of Trump arrest...Washington Post (March 22). https: //www.washingtonpost.com/politics/2023/03/22/trump-arrest-deepfakes/.

[9]. Binder M. (2023) Republicans release AI-generated attack ad. Mashable, April 25 https: //mashable.com/video/republican-attack-ad-biden-reelection-ai.

[10]. Zhou, X., Wang, Y., & Wu, P. (2020). Detecting deepfake videos via frame serialization learning. Paper presented at the 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI).

[11]. Heidari, A., Jafari Navimipour, N., Dag, H., & Unal, M. (2024). Deepfake detection using deep learning methods: A systematic and comprehensive review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(2), e1520.

[12]. Zhou, Y., & Lim, S. N. (2021). Joint audio-visual deepfake detection. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 14800-14809.

[13]. Oorloff, T., Koppisetti, S., Bonettini, N., Solanki, D., Colman, B., Yacoob, Y., ... & Bharaj, G. (2024). Avff: Audio-visual feature fusion for video deepfake detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 27102-27112.

[14]. Ilyas, H., Javed, A., & Malik, K. M. (2023). AVFakeNet: A unified end-to-end Dense Swin Transformer deep learning model for audio–visual​ deepfakes detection. Applied Soft Computing, 136, 110124.

[15]. Roundtree, A. K. (2025). Deepfake Laws Protecting Childhood Safety and Future: An NLP Analysis. In Proceedings of the 24th Interaction Design and Children, pp. 1086-1090.

Cite this article

Chen,W. (2025). Deepfake Technology's Dual Nature: A Review of Security Risk Assessment and Defense Strategies. Applied and Computational Engineering,183,14-20.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Applied Artificial Intelligence Research

ISBN: 978-1-80590-341-3(Print) / 978-1-80590-342-0(Online)
Editor: Hisham AbouGrad
Conference website: https://2025.confmla.org/
Conference date: 3 September 2025
Series: Applied and Computational Engineering
Volume number: Vol.183
ISSN: 2755-2721(Print) / 2755-273X(Online)

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