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Journals / Türkiye Adalet Akademisi Dergisi / 2026 / Sayı: 65

The Evidentiary Value of Visual Data Obtained Through Deep Learning in Criminal Procedure Law: A Legal and Technical Analysis

CEZA MUHAKEMESİ HUKUKUNDA DERİN ÖĞRENME İLE ELDE EDİLEN GÖRSEL VERİLERİN DELİL NİTELİĞİ: HUKUKİ VE TEKNİK BİR ANALİZ

Yalçın Kaplan(Sahil Güvenlik Komutanlığı MEBS Başkanlığı)0000-0002-3414-1771
Pages
95–132
Publication Date
2026-01-20
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In conclusion, the study offers structural recommendations and a model for the integration of AI-based forensic systems in compliance with legal norms, including legal regulations, expert training, ethical guidelines, and human oversight.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines the use of deep learning-based Artificial Intelligence (AI) systems in forensic image analysis and their evidentiary value under Turkish Criminal Procedure Law from an interdisciplinary perspective.
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Key Findings & Data:Despite the technical capacity of AI-supported analyses, their legal admissibility must be assessed within the framework of key legal principles such as lawfulness, transparency, auditability, explainability, and personal data protection.
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Conclusion & Impact:In conclusion, the study offers structural recommendations and a model for the integration of AI-based forensic systems in compliance with legal norms, including legal regulations, expert training, ethical guidelines, and human oversight.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay zekâ · derin öğrenme · görüntü analizi

Abstract

This study examines the use of deep learning-based Artificial Intelligence (AI) systems in forensic image analysis and their evidentiary value under Turkish Criminal Procedure Law from an interdisciplinary perspective. Despite the technical capacity of AI-supported analyses, their legal admissibility must be assessed within the framework of key legal principles such as lawfulness, transparency, auditability, explainability, and personal data protection. The study first outlines deep learning-based image processing techniques and then analyzes their impact on the processes of obtaining and evaluating evidence in criminal proceedings. Within the scope of the principle of freedom of evidence in Turkish law, the legal validity of data obtained through AI systems is evaluated in light of the Constitution, the Criminal Procedure Code (CMK), and the Law on the Protection of Personal Data (KVKK). Specific attention is given to the admissibility of data types such as facial recognition and CCTV footage in court. Furthermore, a comparative legal analysis is conducted with reference to the practices of the European Court of Human Rights, the European Union AI Act, and the Daubert standard in the United States, highlighting the regulatory gaps in Turkish law. The study also explains how the “black box” nature of AI may conflict with the right to defense and the principle of a fair trial, emphasizing the importance of Explainable AI in addressing these concerns. In conclusion, the study offers structural recommendations and a model for the integration of AI-based forensic systems in compliance with legal norms, including legal regulations, expert training, ethical guidelines, and human oversight. By addressing both technical and legal aspects, the study aims to fill a significant gap in the literature.

Özet

Bu çalışma, derin öğrenme tabanlı Yapay Zekâ (YZ) sistemlerinin adli görüntü analizlerinde kullanımı ile bu sistemlerin ceza muhakemesi hukuku açısından delil niteliğini disiplinlerarası bir bakış açısıyla ele almaktadır. YZ destekli analizlerin teknik kapasitesine rağmen, bu sistemlerin hukuki geçerliliği; hukuka uygunluk, şeffaflık, denetlenebilirlik, açıklanabilirlik ve kişisel veri koruması gibi temel ilkeler doğrultusunda değerlendirilmelidir. Çalışmada öncelikle derin öğrenme temelli görüntü işleme teknikleri özetlenmiş, ardından bu tekniklerin ceza yargılamasında delil elde etme ve değerlendirme süreçlerine etkisi incelenmiştir. Türk hukukunda delil serbestisi ilkesi kapsamında YZ ile elde edilen verilerin hukuki geçerliliği Anayasa, CMK ve Kişisel Verilerin Korunması Kanunu (KVKK) çerçevesinde analiz edilmiş; sokakta bulunan kameralardan yüz tanıma ve güvenlik kamerası görüntüsü gibi veri türlerinin mahkemede kullanılabilirliğine ilişkin kriterler tartışılmıştır. Ayrıca Avrupa İnsan Hakları Mahkemesi, Avrupa Birliği AI Act ve ABD Daubert standardı çerçevesindeki uygulamalarla karşılaştırmalı analiz yapılmış; Türkiye’deki düzenleme eksikliklerine dikkat çekilmiştir. YZ’nin “kara kutu” yapısının savunma hakkı ve adil yargılanma ilkeleriyle yaratabileceği çelişkiler açıklanmış ve açıklanabilir yapay zekâ yaklaşımlarının önemi vurgulanmıştır. Sonuç olarak çalışmada, YZ tabanlı adli sistemlerin hukuk düzeniyle uyumlu entegrasyonu için yasal düzenleme, bilirkişi eğitimi, etik ilkeler ve insan denetimi gibi öneriler sunulmuştur. Bu yönüyle çalışma, teknik ve hukuki boyutları birlikte ele alarak alandaki özgün bir boşluğu doldurmayı hedeflemektedir.

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Cite This Article

Kaplan, Y. (2026). The Evidentiary Value of Visual Data Obtained Through Deep Learning in Criminal Procedure Law: A Legal and Technical Analysis. Türkiye Adalet Akademisi Dergisi, Sayı: 65, 95–132. https://doi.org/10.54049/taad.1864107

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References (49)

  1. Akgül M, ‘Adli Bilişim ve Veri Koruma’ (2021) 15(2) Hukuk ve Teknoloji Dergisi 45
  2. Burrell J, ‘How the Machine “Thinks”: Understanding Opacity in Machine Learning Algorithms’ (2016) 3(1) Big Data & Society 1
  3. Antović and Mirković v Montenegro App no 70838/13 (ECtHR, 28 November 2017)
  4. Bochkovskiy A, Wang C-Y and Liao H-YM, ‘YOLOv4: Optimal Speed and Accuracy of Object Detection’ (2020) arXiv:2004.10934 https://arxiv.org/abs/2004.10934
  5. Carpenter v United States 585 US ___ (2018)
  6. Council of Europe, Guidelines on the Responsible Use of Artificial Intelligence (AI) in the Criminal Justice System (CEPEJ, 2020) https://www.coe.int/en/web/cepej/ai-in-the-criminal-justice-system accessed 6 June 2025
  7. Çetin Ö, ‘Yapay Zekâ ve Hukuk: Etik, Hukuki ve Sosyal Boyutlar’ (2024) 5(2) Hukuk ve Teknoloji Dergisi 45
  8. Çetin, A. (2024). Yapay zekâ ve hukuk. Ankara: Adalet Yayınevi
  9. Damaška MR, Evidence Law Adrift (Yale University Press 1997)
  10. Daubert v Merrell Dow Pharmaceuticals, Inc 509 US 579 (1993)
  11. Delfi AS v Estonia App no 64569/09 (ECtHR, 16 June 2015)
  12. Digital Rights Ireland Ltd v Minister for Communications Joined Cases C-293/12 and C-594/12 (CJEU, 8 April 2014)
  13. Doshi-Velez F and Kim B, ‘Towards a Rigorous Science of Interpretable Machine Learning’ (2017) arXiv:1702.08608 https://arxiv.org/abs/1702.08608 accessed 6 June 2025
  14. European Commission, Artificial Intelligence Act: Harmonised Rules on Artificial Intelligence COM(2021) 206 final https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206 accessed 6 June 2025
  15. European Commission, Ethics Guidelines for Trustworthy AI (High-Level Expert Group on AI, 2019) https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai accessed 6 June 2025
  16. European Union Agency for Fundamental Rights, Getting the Future Right – Artificial Intelligence and Fundamental Rights (Publications Office of the European Union 2020)
  17. Goodman B and Flaxman S, ‘EU Regulations on Algorithmic Decision-Making and a “Right to Explanation”’ (2017) 38(3) AI Magazine 50
  18. Gültekin K, ‘Yapay Zekâ Sistemleri ile Elde Edilen Verilerin Ceza Muhakemesinde Delil Niteliği’ (2022) 8(1) Adli Bilişim Dergisi 112
  19. Gürkan S and Aydın M, ‘Biyometrik Verilerin İşlenmesinde Hukuka Uygunluk Kriterleri’ (2023) 2(1) Kişisel Veriler ve Hukuk Dergisi 25
  20. Goodfellow I, Bengio Y and Courville A, Deep Learning (MIT Press 2016)
  21. Hildebrandt M, Law for Computer Scientists and Other Folk (Oxford University Press 2020)
  22. Hildebrandt M, Smart Technologies and the End(s) of Law: Novel Entanglements of Law and Technology (Edward Elgar Publishing 2015)
  23. Jansen H, Krüger F and Bittner K, ‘Application of Deep Learning for Bloodstain Pattern Recognition in Crime Scene Investigation’ (2021) 24(3) Forensic Imaging 110
  24. Kaya A and Yıldız S, ‘Yapay Zekâ Temelli Delillerin Türk Ceza Muhakemesi Hukukunda Kabul Edilebilirliği’ (2022) 80(2) İstanbul Hukuk Mecmuası 245
  25. Kesan JP and Hayes CM, ‘Thinking Through Fairness in Automated Decision-Making: Implications for Administrative Law’ (2019) 104(3) Iowa L Rev 633
  26. Koç A, Ceza Yargılamasında Dijital Delillerin Hukuki Niteliği (Adalet Yayınevi 2021)
  27. Kunter N, Yenisey F and Nuhoğlu A, Ceza Muhakemesi Hukuku (18th edn, Beta Yayıncılık 2019)
  28. Lopez Ribalda and Others v Spain App nos 1874/13 and 8567/13 (ECtHR, 17 October 2019)
  29. Lin Y, Zhang J and Ma T, ‘YOLO-Based Automatic Video Surveillance System for Public Security’ (2021) 4 Forensic Science International: Reports 100229
  30. Selçuk K, ‘Derin Öğrenme Tekniklerinin Adli Bilişimde Kullanımı: Uygulama Alanları ve Etik Sorunlar’ (2023) 8(1) Adli Bilişim Dergisi 51
  31. Selçuk, B. (2023). Adli bilişimde derin öğrenme teknikleri ve uygulamaları. İstanbul: Seçkin Yayıncılık.
  32. Redmon J and others, ‘You Only Look Once: Unified, Real-Time Object Detection’ in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) 779
  33. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 779–788). https://doi.org/10.1109/CVPR.2016.91
  34. Ronneberger O, Fischer P and Brox T, ‘U-Net: Convolutional Networks for Biomedical Image Segmentation’ in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (Springer 2015) 234
  35. Russell S and Norvig P, Artificial Intelligence: A Modern Approach (4th edn, Pearson 2020)
  36. O’Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
  37. S. and Marper v United Kingdom Apps nos 30562/04 and 30566/04 (ECtHR, 4 December 2008)
  38. Schrems II (Data Protection Commissioner v Facebook Ireland Ltd and Maximillian Schrems) Case C-311/18 (CJEU, 16 July 2020)
  39. State v Loomis 881 NW2d 749 (Wis 2016)
  40. Szegedy C and others, ‘Going Deeper with Convolutions’ in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015) 1
  41. Türkiye Barolar Birliği, Adli Bilişim ve Hukuki Boyutları (TBD-2023-01)
  42. Türk Ceza Muhakemesi Kanunu, Resmî Gazete, 17 Aralık 2004, Sayı 2567
  43. Wachter S, Mittelstadt B and Floridi L, ‘Why a Right to Explanation of Automated Decision-Making Does Not Exist in the GDPR’ (2017) 7(2) International Data Privacy Law 76
  44. Wang, H., Li, Y., & Xu, J. (2020). Applications of deep learning in digital forensics: A survey. Forensic Science International: Digital Investigation, 32, 300926. https://doi.org/10.1016/j.fsidi.2019.200926
  45. Yenerer A, ‘Ceza Muhakemesinde Dijital Delillerin Hukuka Uygunluğu’ (2021) 9(2) Journal of Penal Law and Criminology 231
  46. Yıldırım K, ‘Yapay Zekâ Sistemlerinin Delil Değeri: Türk Ceza Muhakemesi Açısından Değerlendirme’ (2022) 13(2) Ceza Hukuku Dergisi 55
  47. Yeung K, ‘Algorithmic Regulation: A Critical Interrogation’ (2018) 12(4) Regulation & Governance 505
  48. Zarsky TZ, ‘The Trouble with Algorithmic Decisions: An Analytic Roadmap to Examine Efficiency and Fairness in Automated and Opaque Decision Making’ (2016) 41(1) Science, Technology, & Human Values 118
  49. Zhang, L., & Zhao, X. (2021). Deep learning in forensic science: Opportunities and challenges. Forensic Science International: Synergy, 3, 100162. https://doi.org/10.1016/j.fsisyn.2021.100162