Reconstructing Reliability Standards for Deepfake Detectors as Electronic Evidence in Indonesia’s Criminal Justice System
DOI:
https://doi.org/10.58812/wslhr.v4i03.3022Keywords:
Artificial Intelligence, Criminal Procedure, Deepfake Detector, Electronic Evidence, Forensic ReliabilityAbstract
The development of deepfakes has transformed the evidentiary problem of video and audio from file authentication into an assessment of the computational method used to determine media authenticity. This study has two objectives: to examine the legal position and limits of the probative value of deepfake detector outputs under Indonesian criminal procedure and to reconstruct an AI Forensic Reliability Test that safeguards authenticity, scientific reliability, and procedural fairness. It applies normative legal research through statutory, conceptual, comparative, and interdisciplinary approaches. The first finding indicates that detector outputs may enter proceedings through electronic evidence, examination reports, and expert testimony, but they do not constitute independent evidence or binary statements of truth. Their value depends on object authenticity, lawful acquisition, methodological validity, error rates, and corroboration. The second finding develops an AI Forensic Reliability Test comprising eight dimensions: object authenticity, model validity, data quality, error rates and calibration, robustness and generalization, traceability and reproducibility, expert competence and independent testing, and procedural fairness and accountability. The framework operationalizes the negative statutory theory of proof, epistemic reliability, machine testimony, due process of law, equality of arms, and accountable algorithms
References
[1] Y. Mirsky and W. Lee, “The creation and detection of deepfakes: A survey,” ACM Comput. Surv., vol. 54, no. 1, pp. 1–41, 2021, doi: 10.1145/3425780.
[2] R. Tolosana, S. Romero-Tapiador, J. Fierrez, and R. Vera-Rodriguez, “Deepfakes and Beyond: A Survey of Face Manipulation and Fake Detection,” Inf. Fusion, vol. 64, no. 1, pp. 131–148, 2020, doi: 10.1016/j.inffus.2020.06.014.
[3] R. Chesney and D. K. Citron, “Deep fakes: A looming challenge for privacy, democracy, and national security,” Calif. Law Rev., vol. 107, no. 6, pp. 1753–1820, 2019, doi: Chesney, Robert and Citron, Danielle Keats, Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security (July 14, 2018). 107 California Law Review 1753 (2019), U of Texas Law, Public Law Research Paper No. 692, U of Maryland Legal Studies Research Paper No. 2018-21, Available at SSRN: https://ssrn.com/abstract=3213954 or http://dx.doi.org/10.2139/ssrn.3213954.
[4] M.-P. Sandoval, M. de Almeida Vau, J. Solaas, and L. Rodrigues, “Threat of deepfakes to the criminal justice system: A systematic review,” Crime Sci., vol. 13, 2024, doi: 10.1186/s40163-024-00239-1.
[5] D. Fallis, “The epistemic threat of deepfakes,” Philos. Technol., vol. 34, no. 4, pp. 623–643, 2021, doi: 10.1007/s13347-020-00419-2.
[6] J. T. Hancock and J. N. Bailenson, “The social impact of deepfakes,” Cyberpsychology, Behav. Soc. Netw., vol. 24, no. 3, pp. 149–152, 2021, doi: 10.1089/cyber.2021.29208.jth.
[7] L. Verdoliva, “Media forensics and deepfakes: An overview,” IEEE J. Sel. Top. Signal Process., vol. 14, no. 5, pp. 910–932, 2020, doi: 10.1109/JSTSP.2020.3002101.
[8] M. S. Rana, M. N. Nobi, B. Murali, and A. H. Sung, “Deepfake detection: A systematic literature review,” IEEE Access, vol. 10, pp. 25494–25513, 2022, doi: 10.1109/ACCESS.2022.3154404.
[9] T. Wang, X. Liao, K. P. Chow, X. Lin, and Y. Wang, “Deepfake detection: A comprehensive survey from the reliability perspective,” ACM Comput. Surv., vol. 57, no. 3, pp. 1–35, 2025, doi: 10.1145/3699710.
[10] L. Stroebel, M. Llewellyn, T. Hartley, T. S. Ip, and M. Ahmed, “A systematic literature review on the effectiveness of deepfake detection techniques,” J. Cyber Secur. Technol., vol. 7, no. 2, pp. 83–113, 2023, doi: 10.1080/23742917.2023.2192888.
[11] A. L. Roth, “Machine testimony,” Yale Law J., vol. 126, no. 7, pp. 1972–2053, 2017.
[12] A. Biedermann and T. Lau, “Decisionalizing the problem of reliance on expert and machine evidence,” Law, Probab. Risk, vol. 23, no. 1, 2024, doi: 10.1093/lpr/mgae007.
[13] “Undang-Undang Nomor 20 Tahun 2025 Tentang Kitab Undang-Undang Hukum Acara Pidana,” Mar. 25, 2025. [Online]. Available: https://peraturan.go.id/id/uu-no-20-tahun-2025
[14] Republik Indonesia, Undang-Undang Nomor 1 Tahun 2024 tentang Perubahan Kedua atas Undang-Undang Nomor 11 Tahun 2008 tentang Informasi dan Transaksi Elektronik. 2024.
[15] S. A. Kusnadi and D. W. S. Putri, “Perlindungan hak privasi dalam penyalahgunaan teknologi deepfake di Indonesia,” J. Rechts Vinding Media Pembin. Huk. Nas., vol. 14, no. 2, pp. 195–210, 2025, doi: 10.33331/rechtsvinding.v14i2.2135.
[16] H. Antoro, D. Budhijanto, and Somawijaya, “Admissibility of artificial intelligence as electronic evidence: Comparative perspectives from Indonesia, the United States, and Japan,” J. IUS Kaji. Huk. dan Keadilan, vol. 14, no. 1, pp. 122–139, 2026, doi: 10.29303/ius.v14i1.1880.
[17] B. van der Sloot and Y. Wagensveld, “Deepfakes: Regulatory Challenges for the Synthetic Society,” Comput. Law Secur. Rev., vol. 46, pp. 1–15, 2022, doi: 10.1016/j.clsr.2022.105716.
[18] E. Hydara, M. Kikuchi, and T. Ozono, “Empirical assessment of deepfake detection: Advancing judicial evidence verification through artificial intelligence,” IEEE Access, vol. 12, pp. 151188–151203, 2024, doi: 10.1109/ACCESS.2024.3480320.
[19] P. M. Marzuki, Penelitian Hukum, Edisi revisi. Jakarta, Indonesia: Kencana, 2021.
[20] S. Soekanto and S. Mamudji, Penelitian Hukum Normatif: Suatu Tinjauan Singkat. Jakarta: Rajawali Pers, 2015.
[21] Republik Indonesia, Undang-Undang Nomor 27 Tahun 2022 tentang Pelindungan Data Pribadi. 2022.
[22] S. Kapoor and A. Narayanan, “Leakage and the reproducibility crisis in machine-learning-based science,” Patterns, vol. 4, no. 9, 2023, doi: 10.1016/j.patter.2023.100804.
[23] F. Abbas and A. Taeihagh, “Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence,” Expert Syst. Appl., vol. 252, 2024, doi: 10.1016/j.eswa.2024.124260.
[24] T. Gebru et al., “Datasheets for datasets,” Commun. ACM, vol. 64, no. 12, pp. 86–92, 2021, doi: 10.1145/3458723.
[25] M. Groh, Z. Epstein, C. Firestone, and R. Picard, “Deepfake detection by human crowds, machines, and machine-informed crowds,” Proc. Natl. Acad. Sci., vol. 119, no. 1, 2022, doi: 10.1073/pnas.2110013119.
[26] E. O. S. Hiariej, Teori dan Hukum Pembuktian. Jakarta, Indonesia: Erlangga, 2012.
[27] P. W. Grimm, M. R. Grossman, and G. V Cormack, “Artificial intelligence as evidence,” Northwest. J. Technol. Intellect. Prop., vol. 19, no. 1, pp. 9–106, 2021.
[28] R. Wexler, “Life, liberty, and trade secrets: Intellectual property in the criminal justice system,” Stanford Law Rev., vol. 70, no. 5, pp. 1343–1429, 2018.
[29] R. V Segate, “Cognitive bias, privacy rights, and digital evidence in international criminal proceedings: Demystifying the double-edged AI revolution,” Int. Crim. Law Rev., vol. 21, no. 2, pp. 242–279, 2021, doi: 10.1163/15718123-bja10048.
[30] K. M. Richmond, S. M. Muddamsetty, T. Gammeltoft-Hansen, H. P. Olsen, and T. B. Moeslund, “Explainable AI and law: An evidential survey,” Digit. Soc., vol. 3, 2024, doi: 10.1007/s44206-023-00081-z.
[31] J. A. Kroll et al., “Accountable algorithms,” Univ. PA. Law Rev., vol. 165, no. 3, pp. 633–705, 2017.
[32] M. Kattnig, A. Angerschmid, T. Reichel, and R. Kern, “Assessing trustworthy AI: Technical and legal perspectives of fairness in AI,” Comput. Law Secur. Rev., vol. 55, 2024, doi: 10.1016/j.clsr.2024.106053.
[33] R. B. da Silva, “Updating the authentication of digital evidence in the International Criminal Court,” Int. Crim. Law Rev., vol. 22, no. 5–6, pp. 941–964, 2022, doi: 10.1163/15718123-bja10083.
[34] F. Romero-Moreno, “Deepfake Detection in Generative AI: A Legal Framework Proposal to Protect Human Rights,” Comput. Law Secur. Rev., vol. 58, p. 106162, 2025, doi: 10.1016/j.clsr.2025.106162.
[35] L. Freeman, “Weapons of war, tools of justice: Using artificial intelligence to investigate international crimes,” J. Int. Crim. Justice, vol. 19, no. 1, pp. 35–53, 2021, doi: 10.1093/jicj/mqab013.
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