Soft Computing Techniques-based Digital Video Forensics for Fraud Medical Anomaly Detection

  • Sunpreet Kaur Nanda Lovely Professional University, Phagwara, Punjab, India
  • Deepika Ghai Lovely Professional University, Phagwara, Punjab, India
  • P. V. Ingole Ram Meghe Institute of Technology & Research, Amravati, India
  • Sagar Pande School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

Abstract

The current pandemic situation has made it important for everyone to wear masks. Digital image forensics plays an important role in preventing medical fraud and in object detection. It is helpful in avoiding the high-risk situations related to the health and security of the individuals or the society, including getting the proper evidence for identifying the people who are not wearing masks. A smart system can be developed based on the proposed soft computing technique, which can be helpful to detect precisely and quickly whether a person wears a mask or not and whether he/ she is carrying a gun. The proposed method gave 100% accurate results in videos used to test such situations. The system was able to precisely differentiate between those wearing a mask and those not wearing a mask. It also effectively detects guns, which can be used in many applications where security plays an important role, such as the military, banks, etc.

Keywords

smart healthcare system, medical imaging, healthcare frauds, MRI imaging, digital image forensics, object detection, YOLO architecture, customized CNN,

References

1. G. Gilboa, N. Sochen, Y.Y. Zeevi, Image enhancement and denoising by complex diffusion processes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(8): 1020–1036, 2004, doi: 10.1109/TPAMI.2004.47.
2. S. Park, S. Yu, M. Kim, K. Park, J. Paik, Dual autoencoder network for retinex-based low-light image enhancement, IEEE Access, 6: 22084–22093, 2018, doi: 10.1109/ACCESS.2018.2812809.
3. W. Fan, K. Wang, F. Cayre, Z. Xiong, Median filtered image quality enhancement and anti-forensics via variational deconvolution, IEEE Transactions on Information Forensics and Security, 10(5): 1076–1091, 2015, doi: 10.1109/TIFS.2015.2398362.
4. C.Y. Li, J.C. Guo, R.M. Cong, Y.W. Pang, B. Wang, Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior, IEEE Transactions on Image Processing, 25(12): 5664–5677, 2016, doi: 10.1109/TIP.2016.2612882.
5. S. Mandal, X.L. Deán-Ben, D. Razansky, Visual quality enhancement in optoacoustic tomography using active contour segmentation priors, IEEE Transactions on Medical Imaging, 35(10): 2209–2217, 2016, doi: 10.1109/TMI.2016.2553156.
6. H. Walker, A. Tough, Facial comparison from CCTV footage: The competence and confidence of the jury, Science & Justice, 55(6): 487–498, 2015, doi: 10.1016/j.scijus.2015.04.010.
7. E. Verolme, A. Mieremet, Application of forensic image analysis in accident investigations, Forensic Science International, 278: 137–147, 2017, doi: 10.1016/j.forsciint.2017.06.039.
8. D. Seckiner, X. Mallett, C. Roux, D. Meuwly, P. Maynard, Forensic image analysis – CCTV distortion and artefacts, Forensic Science International, 285: 77–85. 2018, doi: 10.1016/ j.forsciint.2018.01.024.
9. S. Li, K.K.R. Choo, Q. Sun, W.J. Buchanan, J. Cao, IoT forensics: Amazon echoes as a use case, IEEE Internet of Things Journal, 6(4): 6487–6497, 2019, doi: 10.1109/JIOT.2019.2906946.
10. M. Jerian, S. Paolino, F. Cervelli, S. Carrato, A. Mattei, L. Garofano, A forensic image processing environment for the investigation of surveillance video, Forensic Science International, 167(2–3): 207–212, 2007, doi: 10.1016/j.forsciint.2006.06.048.
11. E.B. Nievas, O.D. Suarez, G.B. García, R. Sukthankar, Violence detection in video using computer vision techniques, [in:] P. Real, D. Diaz-Pernil, H. Molina-Abril, A. Berciano, W. Kropatsch [Eds.], Computer Analysis of Images and Patterns, CAIP 2011, Lecture Notes in Computer Science, vol. 6855, pp. 332–339, 2011, Springer, Berlin, Heidelberg, doi: 10.1007/978-3-642-23678-5_39.
12. D. Gowsikhaa, S. Abirami, Suspicious human activity detection from surveillance videos, International Journal on Internet & Distributed Computing Systems, 2(2): 141–148, 2012.
13. A.H. Lone, F.A. Badroo, K.R. Chudhary, A. Khalique, Implementation of forensic analysis procedures for WhatsApp and Viber Android applications, International Journal of Computer Applications, 128(12): 26–33, 2015, doi: 10.5120/ijca2015906683.
14. J. Kamenicky et al., PIZZARO: Forensic analysis and restoration of image and video data, Forensic Science International, 264: 153–166, 2016, doi: 10.1016/j.forsciint.2016.04.027.
15. Q.Wan, K. Panetta, S. Agaian, A video forensic technique for detecting frame integrity using the human visual system-inspired measure, [in:] 2017 IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1–6, 2017, doi: 10.1109/THS.2017.7943466.
16. M.F.E.M. Senan, S.N.H.S. Abdullah, W.M. Kharudin, N.A.M. Saupi, CCTV quality assessment for forensics facial recognition analysis, [in:] 2017 7th International Conference on Cloud Computing, Data Science & Engineering – Confluence, pp. 649–655, 2017, doi: 10.1109/CONFLUENCE.2017.7943232.
17. H. Kaur, K.R. Choudhary, Digital forensics: implementation and analysis for Google Android framework, [in:] I.M. Alsmadi, G. Karabatis, A. Aleroud [Eds.], Information Fusion for Cyber-Security Analytics, Springer International Publishing, pp. 307–331, 2017.
18. Y. Chen, X. Kang, Z.J. Wang, Q. Zhang, Densely connected convolutional neural network for multi-purpose image forensics under anti-forensic attacks, [in:] Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security, pp. 91–96, June 2018, doi: 10.1145/3206004.3206013.
19. G. Singh, K. Singh, Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation, Multimedia Tools and Applications, 78(9): 11527–11562, 2019, doi: 10.1007/s11042-018-6585-1.
20. P. Zhou, Q. Ding, H. Luo, H. Hou, Violence detection in surveillance video using low-level features, PLoS ONE, 13(10): e0203668, 2018, doi: 10.1371/journal.pone.0203668.
21. M. Ramzan et al., A review on state-of-the-art violence detection techniques, IEEE Access, 7: 107560–107575, 2019, doi: 10.1109/ACCESS.2019.2932114.
22. J. Horváth et al., Anomaly-based manipulation detection in satellite images, [in:] Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 62–71, 2019.
23. B. Peixoto, B. Lavi, J.P.P. Martin, S. Avila, Z. Dias, A. Rocha, Toward subjective violence detection in videos, [in:] ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8276–8280, 2019, doi: 10.1109/ICASSP.2019.8682833.
24. H. Kaur, N. Jindal, Image and video forensics: A critical survey, Wireless Personal Communications, 112: 1281–1302, 2020, doi: 10.1007/s11277-020-07102-x.
25. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, [in:] Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788, 2016, doi: 10.1109/CVPR.2016.91.
26. Kanksha, B. Aman, P. Sagar, M. Rahul, K. Aditya, An intelligent unsupervised technique for fraud detection in health care systems, Intelligent Decision Technologies, 15(1): 127–139, 2021, doi: 10.3233/IDT-200052.
27. S.K. Nanda, D. Ghai, S. Pande, VGG-16-based framework for identification of facemask using video forensics, [in:] D. Gupta, Z. Polkowski, A. Khanna, S. Bhattacharyya, O. Castillo [Eds.], Proceedings of Data Analytics and Management. Lecture Notes on Data Engineering and Communications Technologies, Vol. 91, Springer, Singapore, 2022, doi: 10.1007/978-981-16-6285-0_54.
28. N. Yadav, S.M. Alfayeed, A. Khamparia, B. Pandey, D.N.H. Thanh, S. Pande, HSV model-based segmentation driven facial acne detection using deep learning, Expert Systems, 39(3): e12760, 2022, doi: 10.1111/exsy.12760.
29. A. Kishor, C. Chakraborty, W. Jeberson, Reinforcement learning for medical information processing over heterogeneous networks, Multimedia Tools and Applications, 80: 23983–24004, 2021, doi: 10.1007/s11042-021-10840-0.
30. A. Kishor, C. Chakraborty, W. Jeberson, A novel fog computing approach for minimization of latency in healthcare using machine learning, International Journal of Interactive Multimedia and Artificial Intelligence, 6(7): 7–17, 2020, doi: 10.9781/ijimai.2020.12.004.
31. A. Kishor, C. Chakraborty, Artificial intelligence and internet of things based healthcare 4.0 monitoring system, Wireless Personal Communications, 2021, doi: 10.1007/s11277-021-08708-5.
32. Shershah Movie last scene/Captain Vikram Batra, https://www.youtube.com/watch?v=pbkAa7HTLyE.
33. Jai Jawan: Sushant Singh Rajput Heads For A Different Kind Of Shooting, NDTV, 2017, 2021, https://youtu.be/0sREKfYyiWE.
34. The Basics of Gun Handling | Shooting Tips from SIG SAUER Academy, NSSF – The Firearm Industry Trade Association, 2014, https://youtu.be/r6Nv74nvEWg.
35. S.K. Nanda, D. Ghai, Future of Video Forensics in IoT, [in:] S.L. Tripathi, S. Dwivedi [Eds.], Electronic Devices and Circuit Design: Challenges and Applications in the Internet of Things, Chapter 8, pp. 113–133, CRC Press, 2022.
Published
Sep 6, 2022
How to Cite
NANDA, Sunpreet Kaur et al. Soft Computing Techniques-based Digital Video Forensics for Fraud Medical Anomaly Detection. Computer Assisted Methods in Engineering and Science, [S.l.], sep. 2022. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/447>. Date accessed: 08 dec. 2022. doi: http://dx.doi.org/10.24423/cames.447.
Section
[CLOSED]Scientific Computing and Learning Analytics for Smart Healthcare Systems