DEEPFAKE IN THE PERSPECTIVE OF HYBRID THREATS TO INDONESIA'S NATIONAL RESILIENCE

Authors

  • Bambang Raditya Komando Distrik Militer 0830 Surabaya Author
  • Erfanda Andi Mada Arectya Universitas Muhammadiyah Surabaya image/svg+xml Author
  • Kenneth Sulthon Alafi Al Hallaj Universitas Islam Negeri Sunan Ampel Surabaya image/svg+xml Author
  • Eka Firmansyah Universitas Muhammadiyah Palu image/svg+xml Author
  • Uyun Khikmata Muhandis Universitas Negeri Surabaya image/svg+xml Author

DOI:

https://doi.org/10.67429/ayed2b80

Keywords:

Deepfake, Hybrid Threats, National Resilience, Artificial Intelligence

Abstract

Rapid advancements in deepfake technology have enabled the creation of highly convincing synthetic media, transforming it into a disinformation tool that threatens cybersecurity, public trust, and national resilience. This study analyzes deepfake through the lens of hybrid threats and its implications for Indonesia’s national resilience. Utilizing a qualitative systematic literature review with thematic content analysis, the study synthesized twenty one scholarly publications (2019-2024) from databases such as Scopus and IEEE Xplore. The results indicate that deepfake embodies a hybrid threat by combining information manipulation, technological exploitation, and psychological operations without military confrontation. Its impacts span ideological, political, social, economic, and defense security dimensions, notably through eroding public trust, intensifying social polarization, and weakening institutional legitimacy. Mitigating this threat requires an integrated strategy encompassing AI governance, regulatory adaptation, digital literacy, and multi stakeholder collaboration. Ultimately, this study contributes a conceptual framework linking synthetic media technology with hybrid threat strategic studies, which is highly relevant to the Indonesian context.

References

[1] Altuncu, E., Franqueira, V. N. L., & Li, S. (2024). Deepfake: Definitions, performance metrics and standards, datasets, and a meta-review. Frontiers in Big Data, 7, Article 1400024, pp. 1-23. https://doi.org/10.3389/fdata.2024.1400024

[2] Fernández Gambín, Á., Yazidi, A., Vasilakos, A. V., Frías-Martínez, V., & Haugerud, H. (2024). Deepfakes: Current and future trends. Artificial Intelligence Review, 57(3), Article 64, pp. 1-30. https://doi.org/10.1007/s10462-023-10679-x

[3] Giannopoulos, G., Smith, H., & Theocharidou, M. (2021). The landscape of hybrid threats: A conceptual model (EUR 30585 EN). Publications Office of the European Union. https://doi.org/10.2760/44985

[4] Bergaust, J. C., & Sellevåg, S. R. (2023). Improved conceptualising of hybrid interference below the threshold of armed conflict. Cambridge Review of International Affairs, pp. 169-195. https://doi.org/10.1080/09662839.2023.2267478

[5] Ikhsanni, A., Prasetyo, R., & Wibowo, S. (2024). Cybersecurity dan tata kelola intelijen. Jurnal Kajian Stratejik Ketahanan Nasional, 7(1), pp. 1-14. https://doi.org/10.7454/jkskn.v7i1.10086

[6] Deppe, K., & Schaal, G. S. (2024). Deepfakes, cognitive warfare, and democratic resilience. Frontiers in Political Science, 6, pp. 1-13. https://doi.org/10.3389/fpos.2024.1392658

[7] Abbas, F., & Taeihagh, A. (2024). Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence. Expert Systems with Applications, 252, 124260, pp. 1-22. https://doi.org/10.1016/j.eswa.2024.124260

[8] Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, pp. 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039

[9] Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hrobjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71, pp. 1-9. https://doi.org/10.1136/bmj.n71

[10] Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams. Campbell Systematic Reviews, 18(2), e1230, pp. 1-6. https://doi.org/10.1002/cl2.1230

[11] Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), pp. 93-112. https://doi.org/10.1177/0739456X17723971

[12] Pollock, D., Peters, M. D. J., Khalil, H., McInerney, P., Alexander, L., Tricco, A. C., Evans, C., de Moraes, É. B., & Godfrey, C. M. (2023). Recommendations for the extraction, analysis, and presentation of results in scoping reviews. JBI Evidence Synthesis, 21(3), pp. 520-532. https://doi.org/10.11124/JBIES-22-00123

[13] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), pp. 77-101. https://doi.org/10.1191/1478088706qp063oa

[14] Alanazi, S., & Asif, S. (2024). Exploring deepfake technology: Creation, consequences and countermeasures. Human-Intelligent Systems Integration, 6, pp. 49-60. https://doi.org/10.1007/s42454-024-00054-8

[15] Sandoval, M. P., de Almeida Vau, M., Solaas, J., et al. (2024). Threat of deepfakes to the criminal justice system: A systematic review. Crime Science, 13, Article 41, pp. 1-19. https://doi.org/10.1186/s40163-024-00239-1

[16] Shoaib, M. R., Wang, Z., Ahvanooey, M. T., & Zhao, J. (2023). Deepfakes, misinformation, and disinformation in the era of frontier AI, generative AI, and large AI models. arXiv, pp. 1-15. https://doi.org/10.48550/arXiv.2311.17394

[17] Seow, J. W., Lim, M. K., Phan, R. C. W., & Liu, J. K. (2022). A comprehensive overview of deepfake: Generation, detection, datasets, and opportunities. Neurocomputing, 513, pp. 351-371. https://doi.org/10.1016/j.neucom.2022.09.135

[18] Ma, Y., Zhang, L., Chen, H., & Wang, X. (2024). Public perception of AI-generated misinformation and deepfake content in social media. Telematics and Informatics Reports, 14, 100138, pp. 1-11. https://doi.org/10.1016/j.teler.2024.100138

[19] Novelli, C., Casolari, F., Taddeo, M., & Floridi, L. (2024). AI governance in the era of generative artificial intelligence: Regulatory perspectives and emerging challenges. Philosophy & Technology, 37, Article 107, pp. 1-25. https://doi.org/10.1007/s13347-024-00727-9

[20] Broinowski, A., & Martin, A. (2024). Governing generative artificial intelligence and synthetic media: Policy challenges and regulatory responses. AI and Ethics, pp. 1-14. https://doi.org/10.1007/s43681-024-00542-2

[21] Passos, L. A., Jodas, D. S., Costa, K. A. P., Eskelinen, P., Papa, J. P., & Ferrer, M. A. (2024). A review of deep learning-based approaches for deepfake content detection. Expert Systems, 41(8), e13570, pp. 1-25. https://doi.org/10.1111/exsy.13570

Additional Files

Published

2026-07-20

Issue

Vol. 1 No. 01 (2026)

Section

Articles

How to Cite

DEEPFAKE IN THE PERSPECTIVE OF HYBRID THREATS TO INDONESIA’S NATIONAL RESILIENCE. (2026). H-Veritas: Law and Society Research Journal, 1(01). https://doi.org/10.67429/ayed2b80