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Abstract

Since the past few years, the complexity and heterogeneity of digital crimes has increased exponentially, which has made the digital evidence & digital forensics paramount for both criminal investigation and civil litigation cases. Some of the routine digital forensic analysis tasks are cumbersome and can increase the number of pending cases especially when there is a shortage of domain experts. While the work is not very complex, the sheer scale can be taxing. With the current scenarios and future predictions, crimes are only going to become more complex and the precedent of collecting and examining digital evidence is only going to increase. In this research, we propose an ML based Digital Forensics Software for Triage Analysis called Synthetic Forensic Omnituens (SynFO) that can automate evidence acquisition, extraction of relevant files, perform automated triage analysis and generate a basic report for the analyst. Results of this research show a promising future for automation with the help of Machine Learning.

References

[1] Xiao, J., Li, S., & Xu, Q. (2019). Video-based evidence analysis and extraction in digital forensic investigation. IEEE Access, 7, 55432–55442. https://doi.org/10.1109/ACCESS.2019.2913648.

[2] Morgan, A., & Coughlan, M. (n.d.). Police use of CCTV on the rail network. Trends and Issues in Crime and Criminal Justice [Electronic Resource], (561), 1–18. https://doi.org/10.3316/informit.983683209203954.

[3] Horsman, G. (2019). Tool testing and reliability issues in the field of digital forensics. Digital Investigation, 28, 163–175. https://doi.org/10.1016/j.diin.2019.01.009.

[4] Lillis, D., Becker, B., O’Sullivan, T., & Scanlon, M. (2016). Current challenges and future research areas for digital forensic investigation. ArXiv:1604.03850 [Cs]. Retrieved from http://arxiv.org/abs/1604.03850.

[5] Pasquale, L., Yu, Y., Salehie, M., Cavallaro, L., Tun, T. T., & Nuseibeh, B. (2013). Requirements-driven adaptive digital forensics. 2013 21st IEEE International Requirements Engineering Conference (RE), 340–341. https://doi.org/10.1109/RE.2013.6636745.

[6] Marturana, F., & Tacconi, S. (2013). A Machine Learning-based Triage methodology for automated categorization of digital media. Digital Investigation, 10(2), 193–204. https://doi.org/10.1016/j.diin.2013.01.001.

[7] Al Fahdi, M., Clarke, N. L., Li, F., & Furnell, S. M. (2016). A suspect-oriented intelligent and automated computer forensic analysis. Digital Investigation, 18, 65–76. https://doi.org/10.1016/j.diin.2016.08.001.

[8] Mohammed, H., Clarke, N., & Li, F. (2016). An automated approach for digital forensic analysis of heterogeneous big data. Journal of Digital Forensics, Security and Law. https://doi.org/10.15394/jdfsl.2016.1384.

[9] Kumar, N., Keserwani, P. K., & Samaddar, S. G. (2017). A comparative study of machine learning methods for generation of digital forensic validated data. 2017 Ninth International Conference on Advanced Computing (ICoAC), 15–20. Chennai: IEEE. https://doi.org/10.1109/ICoAC.2017.8441495.

[10] K, A., Grzonkowski, S., & Lekhac, N. A. (2018). Enabling trust in deep learning models: A digital forensics case study. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 1250–1255. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00172.

[11] Johnson, M. K., & Farid, H. (2007). Exposing digital forgeries through specular highlights on the eye. In T. Furon, F. Cayre, G. Doërr, & P. Bas (Eds.), Information Hiding (pp. 311–325). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-77370-2_21.

[12] Makrushin, A., Neubert, T., & Dittmann, J. (2017). Automatic generation and detection of visually faultless facial morphs: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 39–50. Porto, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0006131100390050.

[13] Neubert, T. (2017). Face morphing detection: An approach based on image degradation analysis. In C. Kraetzer, Y.-Q. Shi, J. Dittmann, & H. J. Kim (Eds.), Digital Forensics and Watermarking (Vol. 10431, pp. 93–106). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-64185-0_8.

[14] Rao, Y., & Ni, J. (2016). A deep learning approach to detection of splicing and copy-move forgeries in images. 2016 IEEE International Workshop on Information Forensics and Security (WIFS), 1–6. Abu Dhabi, United Arab Emirates: IEEE. https://doi.org/10.1109/WIFS.2016.7823911.

[15] Huang, T., & Yuan, X. (2018). Detection and classification of various image operations using deep learning technology. 2018 International Conference on Machine Learning and Cybernetics (ICMLC), 50–55. Chengdu: IEEE. https://doi.org/10.1109/ICMLC.2018.8526999.

[16] Tuama, A., Comby, F., & Chaumont, M. (2016). Camera model identification based machine learning approach with high order statistics features. 2016 24th European Signal Processing Conference (EUSIPCO), 1183–1187. Budapest, Hungary: IEEE. https://doi.org/10.1109/EUSIPCO.2016.7760435.

[17] Chatzis, V., Panagiotopoulos, F., & Mardiris, V. (2016). Face to Iris Area Ratio as a feature for children detection in digital forensics applications. 2016 Digital Media Industry & Academic Forum (DMIAF), 121–124. Santorini, Greece: IEEE. https://doi.org/10.1109/DMIAF.2016.7574915.

[18] Senan, M. F. E. M., Abdullah, S. N. H. S., Kharudin, W. M., & Saupi, N. A. M. (2017). CCTV quality assessment for forensics facial recognition analysis. 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence, 649–655. Noida, India: IEEE. https://doi.org/10.1109/CONFLUENCE.2017.7943232.

[19] Amato, G., Falchi, F., Gennaro, C., Massoli, F. V., Passalis, N., Tefas, A., … Vairo, C. (2019). Face verification and recognition for digital forensics and information security. 2019 7th International Symposium on Digital Forensics and Security (ISDFS), 1–6. Barcelos, Portugal: IEEE. https://doi.org/10.1109/ISDFS.2019.8757511.

[20] Bharati, A., Singh, R., Vatsa, M., & Bowyer, K. W. (2016). Detecting facial retouching using supervised deep learning. IEEE Transactions on Information Forensics and Security, 11(9), 1903–1913. https://doi.org/10.1109/TIFS.2016.2561898. [21] Tariq, S., Lee, S., Kim, H., Shin, Y., & Woo, S. S. (2018). Detecting both machine and human created fake face images in the wild. Proceedings of the 2nd International Workshop on Multimedia Privacy and Security, 81–87. Toronto Canada: ACM. https://doi.org/10.1145/3267357.3267367. [22] The go programming language. (n.d.). Retrieved August 25, 2021, from https://golang.org/.

[23] Welcome to Python.org. (n.d.). Retrieved August 25, 2021, from Python.org website: https://www.python.org/

[24] Clark, K. L., & McCabe, F. G. (2004). Go! – A multi-paradigm programming language for implementing multi-threaded agents. Annals of Mathematics and Artificial Intelligence, 41(2–4), 171–206. https://doi.org/10.1023/B:AMAI.0000031195.87297.d9.

[25] Nagpal, A., & Gabrani, G. (2019). Python for data analytics, scientific and technical applications. 2019 Amity International Conference on Artificial Intelligence (AICAI), 140–145. Dubai, United Arab Emirates: IEEE. https://doi.org/10.1109/AICAI.2019.8701341.

[26] Uzun, E., & Sencar, H. T. (2020). Jpg $Scraper$: An Advanced Carver for JPEG Files. IEEE Transactions on Information Forensics and Security, 15, 1846–1857. https://doi.org/10.1109/TIFS.2019.2953382.

[27] Download excel—Buy spreadsheet software | microsoft excel. (n.d.). Retrieved March 23, 2022, from https://www.microsoft.com/en-in/microsoft-365/excel

[28] Numbers. (n.d.). Retrieved March 23, 2022, from Apple (India) website: https://www.apple.com/in/numbers/

[29] Pages. (n.d.). Retrieved March 23, 2022, from Apple (India) website: https://www.apple.com/in/pages/

[30] Transform your word docs with microsoft 365 | microsoft word. (n.d.). Retrieved March 23, 2022, from https://www.microsoft.com/en-in/microsoft-365/word

[31] Writer | libreoffice—Free office suite—Based on openoffice—Compatible with microsoft. (n.d.). Retrieved March 23, 2022, from https://www.libreoffice.org/discover/writer/

[32] Unidoc, Retrieved Match 23, 2022, from GitHub, website: https://github.com/unidoc/unidoc

[33] Dlib c++ library. (n.d.). Retrieved March 23, 2022, from http://dlib.net/

[34] Geitgey, A. (n.d.). Face-recognition: Recognize faces from python or from the command line [Python]. Retrieved from https://github.com/ageitgey/face_recognition

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