A Bibliometrics Analysis of Multimedia Forensics and Deep Learning Research Based on Scopus Index

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Diterbitkan: Sep 20, 2023

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Erika Ramadhani
Universitas Islam Indonesia
Dedy Hariyadi
Universitas Jenderal Achmad Yani

Abstrak

Forensics of digital data are concerned with identifying, acquiring, processing, analysing, and reporting on electronic data. Multimedia forensics focuses on investigating computer crimes using forensic methods. The analysis of multimedia evidence is the role of multimedia forensics, on the other hand. In the analysis, digital evidence is evaluated scientifically to maintain its integrity, to find its source, and to authenticate it. There are several methods in multimedia forensics, such as the implementation of deep learning. The purpose of this research is to conduct bibliometric analysis in multimedia forensics and deep learning by using the data gathered from Scopus index on the keyword “multimedia forensics and deep learning”. The result is 68 relevant papers were found in the range of 2017-2022. The results of this research can be used by researchers as a reference when conducting research and determining the research themes to be pursued.

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Ramadhani, E., & Hariyadi, D. (2023). A Bibliometrics Analysis of Multimedia Forensics and Deep Learning Research Based on Scopus Index. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 4(3), 129 - 133. Diambil dari https://jurnal-itsi.org/index.php/jitsi/article/view/187
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Vol 4 No 3 (2023)
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Referensi

[1] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, How to conduct a bibliometric analysis: An overview and guidelines. J. Bus. Res., vol. 133, pp. 285–296, 2021, doi: https://doi.org/10.1016/j.jbusres.2021.04.070.
[2] P. Weingart, Impact of bibliometrics upon the science system: Inadvertent consequences?, Scientometrics, vol. 62, no. 1, pp. 117–131, 2005, doi: 10.1007/s11192-005-0007-7.
[3] D. Jacobs and P. Ingwersen, A Bibliometric Study of the Publication Patterns in the Sciences of South African Scholars 1981–96, Scientometrics, vol. 47, no. 1, pp. 75–93, 2000, doi: 10.1023/A:1005617825947.
[4] D. F. Thompson and C. K. Walker, A descriptive and historical review of bibliometrics with applications to medical sciences, Pharmacother. J. Hum. Pharmacol. Drug Ther., vol. 35, no. 6, pp. 551–559, 2015.
[5] T. Authors, T. Url, P. Date, and A. This, Digital forensics trends and future, 2014.
[6] Y. Prayudi and A. Ashari, A Study on Secure Communication for Digital Forensics Environment, Int. J. Sci. Eng. Res., vol. 6, no. 1, pp. 1036–1043, 2015, doi: 10.14299/ijser.2015.01.010.
[7] S. Battiato, O. Giudice, and A. Paratore, Multimedia Forensics : discovering the history of multimedia contents, no. June, pp. 23–24, 2016.
[8] A. V Kulkarni, B. Aziz, I. Shams, and J. W. Busse, Comparisons of Citations in Web of Science, Scopus, and Google Scholar for Articles Published in General Medical Journals, JAMA, vol. 302, no. 10, pp. 1092–1096, Sep. 2009, doi: 10.1001/jama.2009.1307.
[9] M. Kusuma, D. Hariyadi, Fazlurrahman, and M. A. Nugroho, The Bibliometric Analysis the Digital Forensics Researcher in Indonesia Based on Garba Rujukan Digital: 2008–2020. in 2021 IEEE Mysore Sub Section International Conference (MysuruCon), Oct. 2021, pp. 13–17. doi: 10.1109/MysuruCon52639.2021.9641641.
[10] D. Salimi, K. Tavasoli, E. Gilani, M. Jouyandeh, and S. Sadjadi, The impact of social media on marketing using bibliometrics analysis, Int. J. Data Netw. Sci., vol. 3, no. 3, pp. 165–184, 2019.
[11] A. B. D. Nandiyanto, D. N. Al Husaeni, and D. F. Al Husaeni, A bibliometric analysis of chemical engineering research using vosviewer and its correlation with Covid-19 pandemic condition, J. Eng. Sci. Technol., vol. 16, no. 6, pp. 4414–4422, 2021.
[12] B. Bayar and M. C. Stamm, Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection, IEEE Trans. Inf. Forensics Secur., vol. 13, no. 11, pp. 2691–2706, 2018, doi: 10.1109/TIFS.2018.2825953.
[13] D. Cozzolino and L. Verdoliva, Noiseprint: A CNN-Based Camera Model Fingerprint, IEEE Trans. Inf. Forensics Secur., vol. 15, pp. 144–159, 2020, doi: 10.1109/TIFS.2019.2916364.
[14] B. Bayar and M. C. Stamm, Design principles of convolutional neural networks for multimedia forensics, IS T Int. Symp. Electron. Imaging Sci. Technol., pp. 77–86, 2017, doi: 10.2352/ISSN.2470-1173.2017.7.MWSF-328.
[15] O. Mayer and M. C. Stamm, Forensic Similarity for Digital Images, IEEE Trans. Inf. Forensics Secur., vol. 15, pp. 1331–1346, 2020, doi: 10.1109/TIFS.2019.2924552.
[16] T. Mittal, U. Bhattacharya, R. Chandra, A. Bera, and D. Manocha, Emotions Don’t Lie: A Deepfake Detection Method using Audio-Visual Affective Cues, ArXiv, vol. abs/2003.06711, 2020.