A Critical Analysis of Criminal Accomplice Provision in Employment Law Violations
DOI:
https://doi.org/10.30996/dih.v0i0.132295Abstract
The increasing use of artificial intelligence (AI), deepfake technology, and advanced medical procedures has transformed the landscape of biometric data, particularly facial features. This study examines the extent to which Indonesia’s Law No. 27 of 2022 on Personal Data Protection (PDP Law) ensures legal certainty for altered biometric facial data, including digitally or medically modified images. Employing a normative juridical research method with statutory and conceptual approaches, the paper interprets legal provisions, evaluates their adequacy, and compares them with international frameworks such as the EU’s General Data Protection Regulation (GDPR) and Singapore’s Personal Data Protection Act (PDPA). Findings reveal that the PDP Law classifies altered facial data as “specific personal data,” mandating explicit consent, robust security measures, and recognition of data subjects’ rights. The law’s extraterritorial scope further extends protection to Indonesian citizens’ data processed abroad. However, enforcement challenges persist, particularly in cross-border contexts and automated profiling. The novelty of this research lies in its focused analysis of altered biometric data as a unique legal category, coupled with comparative insights to address regulatory gaps. The study recommends strengthening implementing regulations, adopting AI-specific safeguards, and enhancing cross-border enforcement cooperation to ensure sustainable protection of biometric privacy in the digital era
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References
Boháček, Matyáš, and Hany Farid. “Protecting President Zelenskyy against Deep Fakes,” June 24, 2022. http://arxiv.org/abs/2206.12043.
Bygrave, L. A. Data Privacy Law: An International Perspective. Oxford University Press, 2014.
Casu, Mirko, Luca Guarnera, Pasquale Caponnetto, and Sebastiano Battiato. “GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusions.” Forensic Science International: Digital Investigation 50 (September 1, 2024). https://doi.org/10.1016/J.FSIDI.2024.301795.
Croitoru, Florinel-Alin, Andrei-Iulian Hiji, Vlad Hondru, Nicolae Catalin Ristea, Paul Irofti, Marius Popescu, Cristian Rusu, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah. “Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook,” November 29, 2024. http://arxiv.org/abs/2411.19537.
Datta, Soumyya Kanti, Shan Jia, and Siwei Lyu. “Exposing Lip-Syncing Deepfakes from Mouth Inconsistencies,” June 3, 2024. http://arxiv.org/abs/2401.10113.
Dolhansky, Brian, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. “The DeepFake Detection Challenge (DFDC) Dataset,” October 28, 2020. http://arxiv.org/abs/2006.07397.
Elizabeth Holm, By A. “In Defense of the Black Box Black Box Algorithms Can Be Useful in Science and Engineering.” Mon. Not. R. As Tron. Soc 364, no. Ml (2020): 3282. http://science.sciencemag.org/.
Floridi, L., Cowls, J., Beltrametti, M., et al. “An Ethical Framework for a Good AI Society.” Minds and Machines 4, no. 28 (2018): 689–707.
Ghilom, Milkias, and Shahram Latifi. “The Role of Machine Learning in Advanced Biometric Systems.” Electronics (Switzerland) 13, no. 13 (July 1, 2024). https://doi.org/10.3390/ELECTRONICS13132667.
Guidotti, Riccardo, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. “A Survey of Methods for Explaining Black Box Models.” ACM Computing Surveys 51, no. 5 (September 30, 2019). https://doi.org/10.1145/3236009.
Heidari, Arash, Nima Jafari Navimipour, Hasan Dag, Samira Talebi, and Mehmet Unal. “A Novel Blockchain-Based Deepfake Detection Method Using Federated and Deep Learning Models.” Cognitive Computation 16, no. 3 (May 1, 2024): 1073–91. https://doi.org/10.1007/S12559-024-10255-7.
Khade, Smita, Swati Ahirrao, Shraddha Phansalkar, Ketan Kotecha, Shilpa Gite, and Sudeep D. Thepade. “Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions.” Inventions 6, no. 4 (December 1, 2021). https://doi.org/10.3390/INVENTIONS6040065.
Kuner, C., Bygrave, L. A., & Docksey, C. The EU General Data Protection Regulation (GDPR): A Commentary. Oxford University Press, 2022.
Li, Boquan, Jun Sun, Christopher M. Poskitt, and Xingmei Wang. “How Generalizable Are Deepfake Image Detectors? An Empirical Study,” August 3, 2024. http://arxiv.org/abs/2308.04177.
O’Byrne, J., and M. E. Cates. “Geometric Theory of (Extended) Time-Reversal Symmetries in Stochastic Processes: I. Finite Dimension.” Journal of Statistical Mechanics: Theory and Experiment 2024, no. 11 (November 30, 2024). https://doi.org/10.1088/1742-5468/ad8f2b.
Romero-Moreno, Felipe. “Deepfake Detection in Generative AI: A Legal Framework Proposal to Protect Human Rights.” Computer Law & Security Review 58 (September 1, 2025): 106162. https://doi.org/10.1016/J.CLSR.2025.106162.
Samin. “Perlindungan Terhadap Kebocoran Data Pribadi Oleh Pengendali Data Melalui Pendekatan Hukum Progresif.” Jurnal Ilmiah Research Student 1, no. 3 (2024): 1–15. https://doi.org/https://doi.org/10.61722/jirs.v1i3.386.
Sandoval, Maria Paz, Maria de Almeida Vau, John Solaas, and Luano Rodrigues. “Threat of Deepfakes to the Criminal Justice System: A Systematic Review.” Crime Science 13, no. 1 (December 1, 2024). https://doi.org/10.1186/S40163-024-00239-1.
Saputra. “Aspek Hukum Telematika Dalam Perlindungan Data Pribadi.” Jurnal Kepastian Hukum Dan Keadilan 1, no. 5 (2023): 54–74.
Schwartz, P. M., & Solove, D. J. “The PII Problem: Privacy and a New Concept of Personally Identifiable Information.” NYU Law Review 6, no. 86 (2020).
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