False Reality: Deepfakes in Terrorist Propaganda and Recruitment
DOI:
https://doi.org/10.70710/sitj.v1i1.5Keywords:
Deepfake Detection, Deepfake Technology, Policy Frameworks, Psychological Impact, Terrorist PropagandaAbstract
Deepfake technology, which leverages artificial intelligence to create hyper-realistic digital fabrications, has emerged as a significant threat across various domains, notably in terrorism. This review critically examines the exploitation of deepfakes in terrorist propaganda and recruitment, presenting a systematic analysis of the technical mechanisms behind their creation and detection, historical and contemporary propaganda methods, and their psychological impacts on audiences. The study identifies key advancements in deepfake detection technologies, such as ensemble learning and convolutional neural networks, which are crucial in distinguishing real from synthetic media. Furthermore, the review highlights the importance of public awareness and psychological resilience as vital countermeasures against deepfake manipulation. Despite technological advancements, significant challenges remain, including the development of real-time detection systems capable of operating in diverse and uncontrolled environments and a comprehensive understanding of the psychological processes affected by deepfake propaganda. The review underscores the urgent need for robust policy frameworks and international cooperation to address the ethical, legal, and security implications of deepfake technology. By integrating technical, psychological, and policy perspectives, this study provides a holistic understanding of deepfake technology's role in modern terrorism and offers insights for developing effective countermeasures. The comprehensive approach aims to contribute to the creation of robust strategies to mitigate the misuse of deepfake technology, ensuring a safer and more trustworthy digital environment.
Downloads
References
Abady, L., Wang, J., Tondi, B., & Barni, M. (2024). A siamese-based verification system for open-set architecture
Abbas, Q., Alghamdi, T., Alsaawy, Y., Alyas, T., Alzahrani, A., Malik, K. I., & Bibi, S. (2023). Reducing Dataset Specificity for Deepfakes Using Ensemble Learning. Computers, Materials and Continua, 74(2), 4261–4276.
Abdullah, M. T., & Ali, N. H. M. (2023). DeepFake Detection Improvement for Images Based on a Proposed Method for Local Binary Pattern of the Multiple-Channel Color Space. International Journal of Intelligent Engineering and Systems, 16(3), 92–104.
Abdulreda, A. S., & Obaid, A. J. (2022). A landscape view of deepfake techniques and detection methods. International Journal of Nonlinear Analysis and Applications, 13(1), 745–755.
Abir, W. H., Khanam, F. R., Alam, K. N., Hadjouni, M., Elmannai, H., Bourouis, S., Dey, R., & Khan, M. M. (2023). Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods. Intelligent Automation and Soft Computing, 35(2), 2151–2169.
Agarwal, A., Singh, R., Vatsa, M., & Noore, A. (2021). MagNet: Detecting Digital Presentation Attacks on Face Recognition. Frontiers in Artificial Intelligence, 4.
Albahar, M., & Almalki, J. (2019). Deepfakes: Threats and countermeasures systematic review. Journal of Theoretical and Applied Information Technology, 97(22), 3242–3250.
Ali, S., DiPaola, D., Lee, I., Sindato, V., Kim, G., Blumofe, R., & Breazeal, C. (2021). Children as creators, thinkers and citizens in an AI-driven future. Computers and Education: Artificial Intelligence, 2.
Amaizu, G. C., Njoku, J. N., Lee, J. M., & Kim, D. S. (2024). Metaverse in advanced manufacturing: Background, applications, limitations, open issues & future directions. In ICT Express (Vol. 10, Issue 2, pp. 233–255). Korean Institute of Communications and Information Sciences.
Amerini, I., Anagnostopoulos, A., Maiano, L., & Celsi, L. R. (2021). Deep learning for multimedia forensics. Foundations and Trends in Computer Graphics and Vision, 12(4), 309–457.
Amin, M. A., Hu, Y., & Hu, J. (2024). Analyzing temporal coherence for deepfake video detection. Electronic Research Archive, 32(4), 2621–2641.
Amin, M. A., Hu, Y., Li, C.-T., & Liu, B. (2024). Deepfake detection based on cross-domain local characteristic analysis with multi-domain transformer. Alexandria Engineering Journal, 91, 592–609.
Appel, M., & Prietzel, F. (2022). The detection of political deepfakes. Journal of Computer-Mediated Communication, 27(4). https://doi.org/10.1093/jcmc/zmac008
Arshed, M. A., Alwadain, A., Faizan Ali, R., Mumtaz, S., Ibrahim, M., & Muneer, A. (2023). Unmasking Deception: Empowering Deepfake Detection with Vision Transformer Network. Mathematics, 11(17).
Ascott, T. (2020). Microfake: How small-scale deepfakes can undermine society. Journal of Digital Media and Policy,
Asha, S., Vinod, P., Amerini, I., & Menon, V. G. (2024). D-Fence layer: an ensemble framework for comprehensive deepfake detection. Multimedia Tools and Applications.
Barabanshchikov, V. A., & Marinova, M. M. (2022). Deepfake as the basis for digitally collaging “impossible faces.” Journal of Optical Technology (A Translation of Opticheskii Zhurnal), 89(8), 448–453.
Biswas, A., Bhattacharya, D., & Kumar, K. A. (2021). DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation. Journal of Information Systems and Telecommunication, 9(35), 161–168.
Brashier, N. M. (2024). Fighting misinformation among the most vulnerable users. In Current Opinion in Psychology (Vol. 57). Elsevier B.V.
Cafiero, F. (2023). Datafying diplomacy: How to enable the computational analysis and support of international negotiations. Journal of Computational Science, 71.
Caldelli, R., Galteri, L., Amerini, I., & Del Bimbo, A. (2021). Optical Flow based CNN for detection of unlearnt deepfake manipulations. Pattern Recognition Letters, 146, 31–37.
Casu, M., Guarnera, L., Caponnetto, P., & Battiato, S. (2024a). GenAI mirage: The impostor bias and the deepfake detection challenge in the era of artificial illusions. In Forensic Science International: Digital Investigation (Vol. 50). Elsevier Ltd.
Casu, M., Guarnera, L., Caponnetto, P., & Battiato, S. (2024b). GenAI mirage: The impostor bias and the deepfake detection challenge in the era of artificial illusions. In Forensic Science International: Digital Investigation (Vol. 50). Elsevier Ltd.
Chen, B., Li, T., & Ding, W. (2022). Detecting deepfake videos based on spatiotemporal attention and convolutional LSTM. Information Sciences, 601, 58–70.
Chen, G.-L., & Hsu, C.-C. (2023). Jointly Defending DeepFake Manipulation and Adversarial Attack Using Decoy Mechanism. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8), 9922–9931.
Chen, H., Li, Y., Lin, D., Li, B., & Wu, J. (2023). Watching the BiG artifacts: Exposing DeepFake videos via Bi-granularity artifacts. Pattern Recognition, 135.
Chen, M., Liao, X., & Wu, M. (2022). PulseEdit: Editing Physiological Signals in Facial Videos for Privacy Protection. IEEE Transactions on Information Forensics and Security, 17, 457–471.
Chhabra, S., Thakral, K., Mittal, S., Vatsa, M., & Singh, R. (2024). Low-Quality Deepfake Detection via Unseen Artifacts. IEEE Transactions on Artificial Intelligence, 5(4), 1573–1585.
Coccomini, D. A., Caldelli, R., Falchi, F., & Gennaro, C. (2023). On the Generalization of Deep Learning Models in Video Deepfake Detection. Journal of Imaging, 9(5).
Cowles, K., Miller, R., & Suppok, R. (2024). When Seeing Isn’t Believing: Navigating Visual Health Misinformation through Library Instruction. Medical Reference Services Quarterly, 43(1), 44–58.
de Ruiter, A. (2021). The Distinct Wrong of Deepfakes. Philosophy and Technology, 34(4), 1311–1332.
Ding, F., Zhu, G., Li, Y., Zhang, X., Atrey, P. K., & Lyu, S. (2022). Anti-Forensics for Face Swapping Videos via Adversarial Training. IEEE Transactions on Multimedia, 24, 3429–3441.
Dong, J., Wang, Y., Lai, J., & Xie, X. (2023). Restricted Black-Box Adversarial Attack Against DeepFake Face Swapping. IEEE Transactions on Information Forensics and Security, 18, 2596–2608.
Ferreira, S., Antunes, M., & Correia, M. E. (2021a). A dataset of photos and videos for digital forensics analysis using machine learning processing. Data, 6(8).
Ferreira, S., Antunes, M., & Correia, M. E. (2021b). Exposing manipulated photos and videos in digital forensics analysis. Journal of Imaging, 7(7).
Fu, H., Gong, M., Wang, C., Batmanghelich, K., Zhang, K., & Tao, D. (2019). Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2422–2431.
Ganguly, S., Ganguly, A., Mohiuddin, S., Malakar, S., & Sarkar, R. (2022). ViXNet: Vision Transformer with Xception Network for deepfakes based video and image forgery detection. Expert Systems with Applications, 210.
Giessmann, H. J. (2002). Media and the Public Sphere: Catalyst and Multiplier of Terrorism? Media Asia, 29(3), 134–136.
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Networks. Science Robotics, 3(January), 2672–2680.
Guo, J., Zhao, Y., & Wang, H. (2023). Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing. Applied Sciences (Switzerland), 13(13).
Gupta, P., Ding, B., Guan, C., & Ding, D. (2024). Generative AI: A systematic review using topic modelling techniques. Data and Information Management, 8(2).
Hameleers, M., van der Meer, T. G. L. A., & Dobber, T. (2024a). Distorting the truth versus blatant lies: The effects of different degrees of deception in domestic and foreign political deepfakes. Computers in Human Behavior, 152.
Hameleers, M., van der Meer, T. G. L. A., & Dobber, T. (2024b). They Would Never Say Anything Like This! Reasons To Doubt Political Deepfakes. European Journal of Communication, 39(1), 56–70.
Harbinja, E., Edwards, L., & McVey, M. (2023). Governing ghostbots. Computer Law and Security Review, 48.
Hussain, S., Neekhara, P., Dolhansky, B., Bitton, J., Ferrer, C. C., Mcauley, J., & Koushanfar, F. (2022). Exposing Vulnerabilities of Deepfake Detection Systems with Robust Attacks. Digital Threats: Research and Practice, 3(3).
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020). Analyzing and Improving the Image Quality of StyleGAN. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8107–8116.
Kietzmann, J., Lee, L. W., McCarthy, I. P., & Kietzmann, T. C. (2020). Deepfakes: Trick or treat? Business Horizons, 63(2), 135–146.
Kirchengast, T. (2020). Deepfakes and image manipulation: criminalisation and control. Information and Communications Technology Law, 308–323.
Kong, S. C., Cheung, M. Y. W., & Tsang, O. (2024). Developing an artificial intelligence literacy framework: Evaluation of a literacy course for senior secondary students using a project-based learning approach. Computers and Education: Artificial Intelligence, 6.
Lakhani, S. (2023). When Digital and Physical World Combine: The Metaverse and Gamification of Violent Extremism. Perspectives on Terrorism, 17(2), 108–125.
Lee, J., & Park, J. (2023). AI as “Another I”: Journey map of working with artificial intelligence from AI-phobia to AI-preparedness. Organizational Dynamics, 52(3).
Liu, H., Zhou, W., Chen, D., Fang, H., Bian, H., Liu, K., Zhang, W., & Yu, N. (2023). Coherent adversarial deepfake video generation. Signal Processing, 203.
Naskar, G., Mohiuddin, S., Malakar, S., Cuevas, E., & Sarkar, R. (2024). Deepfake detection using deep feature stacking and meta-learning. Heliyon, 10(4).
Newman, E. J., & Schwarz, N. (2024). Misinformed by images: How images influence perceptions of truth and what can be done about it. In Current Opinion in Psychology (Vol. 56). Elsevier B.V.
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2022). Deep learning for deepfakes creation and detection: A survey. Computer Vision and Image Understanding, 223.
Pu, W., Hu, J., Wang, X., Li, Y., Hu, S., Zhu, B., Song, R., Song, Q., Wu, X., & Lyu, S. (2022). Learning a deep dual-level network for robust DeepFake detection. Pattern Recognition, 130.
Tran, V.-N., Kwon, S.-G., Lee, S.-H., Le, H.-S., & Kwon, K.-R. (2023). Generalization of Forgery Detection With Meta Deepfake Detection Model. IEEE Access, 11, 535–546.
Van der Sloot, B., & Wagensveld, Y. (2022). Deepfakes: regulatory challenges for the synthetic society. Computer Law and Security Review, 46.
Vizoso, Á., Vaz-álvarez, M., & López-García, X. (2021). Fighting deepfakes: Media and internet giants’ converging and diverging strategies against hi-tech misinformation. Media and Communication, 9(1), 291–300.
Xu, P., Ma, Z., Mei, X., & Shen, J. (2024). Detecting facial manipulated images via one-class domain generalization. Multimedia Systems, 30(1).
Yang, W., Zhou, X., Chen, Z., Guo, B., Ba, Z., Xia, Z., Cao, X., & Ren, K. (2023). AVoiD-DF: Audio-Visual Joint Learning for Detecting Deepfake. IEEE Transactions on Information Forensics and Security, 18, 2015–2029.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Security Intelligence Terrorism Journal (SITJ)

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.