Integrating artificial intelligence in forensic science
DOI:
https://doi.org/10.15503/emet2023.15.28Keywords:
Artificial intelligence, forensic science, AI applications in forensic science, machine learning for evidence analysis, crime scene reconstruction with AIAbstract
Thesis. The thesis of this article is to explore the integration of artificial intelligence (AI) methodology in forensic science. An assessment of the potential implications of AI for improving investigative processes and outcomes will also be addressed.
Concept. The concept focuses on exploring the application of AI technologies in ar- eas of science such as analysing evidence recognising patterns and supporting decision- making systems. The article emphasises the practical use of AI algorithms in investiga- tions. on exploring how artificial intelligence technologies can be implemented in various aspects of forensic science.
Results. An analysis of existing literature and case studies shows that integrating AI into forensic science can improve efficiency, accuracy, and objectivity in investigations. AI tools can automate tasks analyse datasets and identify patterns that might be challeng-
16 “ABoUT The INTeRNeT” - TheoRy
ing for humans to detect. However utilising AI in forensic science poses challenges like algorithms, privacy issues with data handling, and the necessity for oversight. Further- more, when employing intelligence for inquiries it is essential to prioritise transparency, and accountability and uphold integrity in decision-making processes.
Originality. This article adds to the discussion about integrating AI into science by of- fering a thorough examination of its potential advantages and obstacles faced along, with ethical concerns. By merging research findings and offering perspectives on emerging trends we can gain insights, into the impact of AI on advancing investigations in the years ahead. Additionally, the theoretical structure presented here establishes a foundation for research studies and practical implementations, within the field of forensic science.
Keywords: Artificial intelligence, forensic science, AI applications in forensic science, machine learning for evidence analysis, crime scene reconstruction with AI
Downloads
References
Ahmed Alaa el-Din, e. (2022). Artificial intelligence in forensic science: Invasion or revolu- tion? Egyptian Society of Clinical Toxicology Journal, 10(2), 20-32.
Almusayli, A., Zia, T., & Qazi, e. U. h. (2024). Drone forensics: An innovative approach to the forensic investigation of drone accidents based on digital twin technology. Technolo- gies, 12(1), 11.
Aradhya, h. R. (2019). object detection and tracking using deep learning and artificial in- telligence for video surveillance applications. International Journal of Advanced Computer Science and Applications, 10(12).
Beall’s List – of potential predatory journals and publishers. (n.d.), Retrieved on July 10, 2024. https://beallslist.net/
Bornik, A., Urschler, M., Schmalstieg, D., Bischof, h., Krauskopf, A., Schwark, T., ... & yen, K. (2018). Integrated computer-aided forensic case analysis, presentation, and docu- mentation based on multimodal 3D data. Forensic Science International, 287, 12-24.
Brady, S. P. (2012). Role of the forensic process in investigating structural failure. Journal of Performance of Constructed Facilities, 26(1), 2-6.
Brettell, T. A., Butler, J. M., & Saferstein, R. (2005). Forensic science. Analytical Chemistry, 77(12), 3839-3860.
Brown, R., & Lee, C. (2024). Deep learning for fingerprint identification: State-of-the-art and challenges. Forensic Computing Review, 18(3), 210-225.
Corvo, M. F., Cathcart, K., Levstein, I., & Compton, S. (2011) Comparison and evaluation of crime scene reconstruction software using visual statement FX3, 3d Eyewitness and Crime Zone.
Dath, C. (2017). Crime scenes in virtual reality: A user centered study.
Dunsin, D., Ghanem, M. C., ouazzane, K., & Vassilev, V. (2024). A comprehensive analysis
of the role of artificial intelligence and machine learning in modern digital forensics and
incident response. Forensic Science International: Digital Investigation, 48, 301675.
Article 3: Definitions. (2024). European Parliament.
Fähndrich, J., honekamp, W., Povalej, R., Rittelmeier, h., Berner, S., & Labudde, D. (2023).
Digital forensics and strong AI: A structured literature review. Forensic Science Internation-
al: Digital Investigation. https://doi.org/10.1016/j.fsidi.2023.301617
Garcia, M., & Patel, S. (2014). Computer vision techniques for shoe print analysis. Interna-
tional Journal of Forensic Sciences, 30(1), 45-58.
Griffiths, G., Liscio, e., Guryn, h., Le, Q., Northfield, D., & Williams, G. A. (2021). Inter-
observer error for area of origin analysis using FARo Zone 3D. Science & Justice, 61(3),
-298.
Guo, S., Wang, S., yang, Z., Wang, L., Zhang, h., Guo, P., Gao, y., & Guo, J. (2022). A review
of deep learning-based visual multi-object tracking algorithms for autonomous driving.
Applied Sciences, 12(21), 10741.
Gupta, S., Sharma, M. V., & Johri, P. (2020). Artificial intelligence in forensic science. Inter-
national Research Journal of Engineering and Technology, 7(5), 7181-7184.
hassan, M., Wang, y., Wang, D., Li, D., Liang, y., Zhou, y., & Xu, D. (2021). Deep learning
analysis and age prediction from shoeprints. Forensic Science International, 327, 110987. Jadhav, e. B., Sankhla, M. S., & Kumar, R. (2020). Artificial intelligence: advancing automation in forensic science & criminal investigation. Journal of Seybold Report ISSN NO, 1533, 9211. Jeong, D. (2020). Artificial intelligence security threat, crime, and forensics: Taxonomy and open
issues. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3029280
Jones, G. M., Winster, S. G., & Valarmathie, P. (2022). An advanced integrated approach in
mobile forensic investigation. Intelligent Automation & Soft Computing, 33(1).
Le, Q., & Liscio, e. (2019). A comparative study between FARo Scene and FARo Zone 3D
for area of origin analysis. Forensic Science International, 301, 166-173.
LeCun, y., Bengio, y., & hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Liu, X., Lu, W., Zhang, Q., huang, J., & Shi, y. Q. (2019). Downscaling factor estimation
on pre- JPEG compressed images. IEEE Transactions on Circuits and Systems for Video Technology, 30(3), 618-631.
Majeed, F., Khan, F.Z., Nazir, M., Iqbal, Z., Alhaisoni, M., Tariq, U., Khan, M.A., Kadry, S. (2022). Investigating the efficiency of deep learning based security system in a real-time environment using yoLov5. Sustainable Energy Technologies and Assessments, 53, 102603.
Mansoor, N., & Iliev, A. (2023). ‘Artificial intelligence in forensic science’. In Future of Infor- mation and Communication Conference (pp. 155-163). Cham: Springer Nature Switzerland. Martin, S. M. (2019). Artificial intelligence, mixed reality, and the redefinition of the class-
room. Rowman & Littlefield.
Milani, S., Fontani, M., Bestagini, P., Barni, M., Piva, A., Tagliasacchi, M., & Tubaro, S.
(2012). An overview on video forensics. APSIPA Transactions on Signal and Information
Processing, 1, e2.
Pandey, R. C., Agrawal, R., Singh, S. K., & Shukla, K. K. (2015). ‘Passive copy move forgery
detection using SURF, hoG and SIFT features’. In Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014: Vol- ume 1 (pp. 659-666). Springer International Publishing.
Pramanik, M. I., Lau, R. y., yue, W. T., ye, y., & Li, C. (2017). Big data analytics for security and criminal investigations. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(4), e1208.
Prasad, S., & Ramkumar, B. (2016, May). ‘Passive copy-move forgery detection using SIFT, hoG and SURF features’. In 2016 IEEE International Conference on Recent Trends in Elec- tronics, Information & Communication Technology (RTEICT) (pp. 706-710). IEEE.
Protsyuk, I., Melnik, A. V., Nothias, L. F., Rappez, L., Phapale, P., Aksenov, A. A., Bousli- mani, A., Ryazanov, A., Dorrestein, P. C., & Alexandrov, T. (2018). 3D molecular cartog- raphy using LC–MS facilitated by Optimus and Ili software. *Nature Protocols.
Rajshekar, M. (2017). Application of 3D scanning technology in forensic investigation of bite- marks. University Of Tasmania.
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson. Sheppard, K., Cassella, J., & Fieldhouse, S. (2016). how technology is revolutionising crime scene capture and presentation visualising a crime scene using Novel crime scene docu-
mentation technology. CSeye, 2016(Jan), 16-24.
Siegel, J. A. (2024). Forensic science. Encyclopedia Britannica. https://www.britannica.com/
science/forensic-science
Smith, J., & Doe, A. (2023). Advances in AI-Assisted DNA Profiling. Journal of Forensic Ge-
netics, 20(2), 87-102.
Stoney, D. A., & Stoney, P. L. (2015). Critical review of forensic trace evidence analysis and
the need for a new approach. Forensic Science International, 251, 159-170.
Sun, D., Zhang, X., Choo, K. K. R., hu, L., & Wang, F. (2021). NLP-based digital forensic
investigation platform for online communications. Computers & Security, 104, 102210. Szeliski, R. (2022). Computer vision: algorithms and applications. Springer Nature.
Tredinnick, R., Smith, S., & Ponto, K. (2019). A cost-benefit analysis of 3D scanning technol-
ogy for crime scene investigation. Forensic Science International: Reports, 1, 100025.
Villa, C., Lynnerup, N., & Jacobsen, C. (2023). A virtual, 3D multimodal approach to victim
and crime scene reconstruction. Diagnostics, 13(17), 2764.
Win, K. N., Li, K., Chen, J., Viger, P. F., & Li, K. (2020). Fingerprint classification and iden-
tification algorithms for criminal investigation: A survey. Future Generation Computer
Systems, 110, 758-771.
Xiao, Jianyu, Shancang Li, & Qingliang Xu. (2019). Video-based evidence analysis and ex-
traction in digital forensic investigation. IEEE Access, 7, 55432-55442.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Angelika Dudek, Anna Dąbek, Iwona Zborowska, Jakub Lichosik
This work is licensed under a Creative Commons Attribution 4.0 International License.
