DIGITAL FORENSICS IN PHOTOGRAPHY EDUCATION
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6768Keywords:
Digital Forensics, Photography Education, Image Authenticity, AI Verification, Ethical Creativity, Forensic Literacy, Digital IntegrityAbstract [English]
The digital photographic history has transformed the principle of authenticity and novel educational patterns are to be established based on balanced models of creativity and forensic consciousness. As image manipulation grows and the images created by AI become more sophisticated, however, the education of photography should be not only artistically sophisticated but also analytical and morally capable. Including the digital forensics course in the curriculum, the learners will be given an opportunity to acquire the understanding of the perception of pictures as aesthetic and evidential objects and relate visual semiotics to the mechanism of data verification. Through the approaches of metadata inspection, analysis of error levels, and artificial intelligence check-up of authenticity, the students end up having a moderate opinion regarding the compatibility of creative expression and digital integrity. Based on the experimental outcomes, the precision, perceptive observation and ethical judgment are increased significantly once the technologies in the field of forensics are incorporated into innovative learning environments. This combination of art, science, and ethics in this model creates a species of photographers that is highly accurate in their technicality, highly philosophical in their thinking capabilities and ethically accountable. This paper intends to capture the essence of digital forensics as an essential part of learning by introducing authenticity as an interactive process of creativity and validation as one of the fundamental aspects of becoming a responsible and well-educated visual practitioner.
References
Aarts, R., Van Wanrooij, L., Bloemen, E., and Smid, G. E. (2019). Expert Medico-Legal Reports: The Relationship Between Levels of Consistency and Judicial Outcomes in Asylum Seekers in the Netherlands. Torture Journal, 29(1), 36–46. https://doi.org/10.7146/torture.v29i1.111286 DOI: https://doi.org/10.7146/torture.v29i1.111205
Al-Sabaawi, A. (2020). Digital Forensics for Infected Computer Disk and Memory: Acquire, Analyse, and Report. In Proceedings of the IEEE Asia-Pacific Conference on Computer Science and Data Engineering. https://doi.org/10.1109/CSDE50874.2020.9383221 DOI: https://doi.org/10.1109/CSDE50874.2020.9411614
Bloemen, E. M., Rosen, T., Schiroo, J. A. C., Clark, S., Mulcare, M. R., Stern, M. E., Mysliwiec, R., Flomenbaum, N. E., Lachs, M. S., and Hargarten, S. (2016). Photographing Injuries in the Acute Care Setting: Development and Evaluation of a Standardized Protocol for Research, Forensics, and Clinical Practice. Academic Emergency Medicine, 23(6), 653–659. https://doi.org/10.1111/acem.12936 DOI: https://doi.org/10.1111/acem.12955
Casino, F., Dasaklis, T., Spathoulas, G., Anagnostopoulos, M., Ghosal, A., Borocz, I., and Patsakis, C. (2022). Research Trends, Challenges, and Emerging Topics in Digital Forensics: A Review of Reviews. IEEE Access, 10, 25464–25493. https://doi.org/10.1109/ACCESS.2022.3152064 DOI: https://doi.org/10.1109/ACCESS.2022.3154059
Castillo Camacho, and Wang, K. (2021). A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics. Journal of Imaging, 7(4), Article 69. https://doi.org/10.3390/jimaging7040069 DOI: https://doi.org/10.3390/jimaging7040069
De Meijer, P. P. G., Karlsson, J., Laprade, R. F., Verhaar, J. A. N., and Wijdicks, C. A. (2012). A Guideline to Medical Photography: A Perspective on Digital Photography in an Orthopaedic Setting. Knee Surgery, Sports Traumatology, Arthroscopy, 20, 2606–2611. https://doi.org/10.1007/s00167-012-1920-8 DOI: https://doi.org/10.1007/s00167-012-2173-5
Dubey, H., Bhatt, S., and Negi, L. (2023). Digital Forensics Techniques and Trends: A Review. International Arab Journal of Information Technology, 20(5), 644–654. https://doi.org/10.34028/iajit/20/5/4 DOI: https://doi.org/10.34028/iajit/20/4/11
Durall, R., Keuper, M., Pfreundt, F. J., and Keuper, J. (2019). Unmasking Deepfakes with Simple Features. arXiv. https://arxiv.org/abs/1911.00686
Harting, M. T., DeWees, J. M., Vela, K. M., and Khirallah, R. T. (2015). Medical photography: Current Technology, Evolving Issues and legal Perspectives. International Journal of Clinical Practice, 69(4), 401–409. https://doi.org/10.1111/ijcp.12580 DOI: https://doi.org/10.1111/ijcp.12627
Jafar, M. T., Ababneh, M., Al-Zoube, M., and Elhassan, A. (2020). Forensics and Analysis of Deepfake Videos. In Proceedings of the IEEE 11th International Conference on Information and Communication Systems (53–58). https://doi.org/10.1109/ICICS49469.2020.239525 DOI: https://doi.org/10.1109/ICICS49469.2020.239493
Johnson, C., Davies, R., and Reddy, M. (2022). Using Digital Forensics in Higher Education to Detect Academic Misconduct. International Journal for Educational Integrity, 18, Article 12. https://doi.org/10.1007/s40979-022-00101-1 DOI: https://doi.org/10.1007/s40979-022-00104-1
Li, Y., Yang, X., Sun, P., Qi, H., and Lyu, S. (2020). Celeb-DF: A Large-Scale Challenging Dataset for Deepfake Forensics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (3207–3216). https://doi.org/10.1109/CVPR42600.2020.00323 DOI: https://doi.org/10.1109/CVPR42600.2020.00327
Nguyen, T. T., Nguyen, C. M., Nguyen, D. T., Nguyen, D. T., and Nahavandi, S. (2019). Deep Learning for Deepfakes Creation and Detection. arXiv.
Niyishaka, P., and Bhagvati, C. (2018). Digital Image Forensics Technique for Copy-Move Forgery Detection Using DoG and ORB. In Proceedings of the International Conference on Computer Vision and Graphics (472–483). Springer. https://doi.org/10.1007/978-3-319-95948-0_42 DOI: https://doi.org/10.1007/978-3-030-00692-1_41
Parekh, M., and Jani, S. (2018). Memory Forensic: Acquisition and Analysis of Memory and its Tools Comparison. In Communications in Integrated Networks and Signal Processing (90–95). Springer. https://doi.org/10.1007/978-981-13-7091-5_9 DOI: https://doi.org/10.29121/ijetmr.v5.i2.2018.618
Rehman Javed, A., Ahmed, W., Alazab, M., Jalil, Z., Kifayat, K., and Gadekallu, T. R. (2022). A Comprehensive Survey on Computer Forensics: State-Of-The-Art, Tools, Techniques, Challenges, and Future Directions. IEEE Access, 10, 11065–11089. https://doi.org/10.1109/ACCESS.2022.3146559 DOI: https://doi.org/10.1109/ACCESS.2022.3142508
Zoltie, T. (2013). Professional Development in Medico-Legal Photography: Understanding the Importance of a Clinical photographer’s Role. Journal of Visual Communication in Medicine, 36(3–4), 82–85. https://doi.org/10.3109/17453054.2013.846125 DOI: https://doi.org/10.3109/17453054.2013.790010
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Bhavuk Samrat, Bhawna Kaushik, Dr. Peeyush Kumar Gupta, Dr. S Igni Sabasti Prabu, Sahil Suri, Pradnya Yuvraj Patil
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
