Detection of Copy-Move Forgery (CMF) in Videos through the Application of a Machine Learning Algorithm
Keywords:
Copy-Move forgery (CMF), weiner filter (WF), threshold based image segmentation (TbIS), integrated stochastic random neighbouring approach (ISRNA)Abstract
One of the most important tasks in digital forensics to find instances of modified content is the detection of copy-move forgery (CMF) in videos. Copy-move forgery includes taking a section of a video, pasting it into another movie, and then hiding or changing that section. As a consequence of advancements in network technology, low-cost multimedia devices, intelligent image or video editing software, and broad adoption of digital multimedia coding standards, the number of applications for digital multimedia has significantly risen in recent years. Establishing if a video is legitimate or not is one of the trickiest areas of video forensics. This may be a crucial responsibility when recordings are used as primary evidence to influence decisions, such as in a court of law. Therefore, we provide a novel machine learning-based copy-move forgery detection technique in this research. Weiner filter is first used to gather and pre-process video data. The pre-processed video data are then segmented using a threshold-based technique to image segmentation. Finally, we suggest a novel integrated stochastic random neighbouring approach (ISRNA) for categorizing videos. Our suggested technique is compared and contrasted with traditional ways to demonstrate the efficacy of the suggested method. Results from experiments show that our suggested strategy performs better than traditional ways.
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Chalamalasetty, S. P., & Giduturi, S. R. (2021). Research perception towards copy-move image forgery detection: challenges and future directions. International Journal of Image and Graphics, 21(04), 2150054.
Jaiswal, A. K., & Srivastava, R. (2022). Detection of copy-move forgery in digital image using multi-scale, multi-stage deep learning model. Neural Processing Letters, 54(1), 75-100.
Suresh, G., & Rao, C. S. (2020). Copy move forgery detection through differential excitation component-based texture features. International Journal of Digital Crime and Forensics (IJDCF), 12(3), 27-44.
Gajjar, P., Saxena, A., Shah, H., Kikani, N., Lakhani, K., Shah, P., & Limbachiya, K. (2022, August). Copy Move Forgery Detection: The Current Implications and Contemporary Practices. In Journal of Physics: Conference Series (Vol. 2325, No. 1, p. 012050). IOP Publishing.
Raskar, P. S., & Shah, S. K. (2022). VFDHSOG: Copy-Move Video Forgery Detection Using Histogram of Second Order Gradients. Wireless Personal Communications, 122(2), 1617-1654.
Gayathri, K. S., & Deepthi, P. S. AN OVERVIEW OF COPY MOVE FORGERY DETECTION APPROACHES.
Vyas, A. ., & Sharma, D. A. . (2020). Deep Learning-Based Mango Leaf Detection by Pre-Processing and Segmentation Techniques. Research Journal of Computer Systems and Engineering, 1(1), 11–16. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/18
Vijayakumar, P., Ahamed, S. B., Anitha, N., Yuvaraj, R., Gulati, K., & Kshirsagar, P. R. (2022, May). Machine learning algorithm for improving the efficiency of forgery detection. In AIP Conference Proceedings (Vol. 2393, No. 1). AIP Publishing.
Armas Vega, E. A., González Fernández, E., Sandoval Orozco, A. L., & García Villalba, L. J. (2021). Copy-move forgery detection technique based on discrete cosine transform blocks features. Neural Computing and Applications, 33, 4713-4727.
Jaiswal, A. K., & Srivastava, R. (2022). Detection of copy-move forgery in digital image using multi-scale, multi-stage deep learning model. Neural Processing Letters, 54(1), 75-100.
Elaskily, M. A., Elnemr, H. A., Sedik, A., Dessouky, M. M., El Banby, G. M., Elshakankiry, O. A., ... & Abd El-Samie, F. E. (2020). A novel deep learning framework for copy-moveforgery detection in images. Multimedia Tools and Applications, 79, 19167-19192.
KOSHY, L., & SHYRY, P. (2023). Detection of Copy Move Forgery Using Rootsift and Feature Point Matching.
Dhiman, O. ., & Sharma, D. A. . (2020). Detection of Gliomas in Spinal Cord Using U-Net++ Segmentation with Xg Boost Classification. Research Journal of Computer Systems and Engineering, 1(1), 17–22. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/20
Aparicio-Díaz, E., Cumplido, R., Pérez Gort, M. L., & Feregrino-Uribe, C. (2019). Temporal copy-move forgery detection and localization using block correlation matrix. Journal of Intelligent & Fuzzy Systems, 36(5), 5023-5035.
Abiodun, O. I., Alawida, M., Omolara, A. E., & Alabdulatif, A. (2022). Data provenance for cloud forensic investigations, security, challenges, solutions and future perspectives: A survey. Journal of King Saud University-Computer and Information Sciences.
Nehe, M. K. S., Birajdar, M. S. R., Ugale, M. M. K., Ugale, S. S., & Shedge, M. K. N. (2019). Framework for Image Forgery Detection. Framework, 6(11).
Yang, J., Liang, Z., Gan, Y., & Zhong, J. (2021). A novel copy-move forgery detection algorithm via two-stage filtering. Digital Signal Processing, 113, 103032.
Allauddin Mulla, R. ., Eknath Pawar, M. ., S. Banait, S. ., N. Ajani, S. ., Pravin Borawake, M. ., & Hundekari, S. . (2023). Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 278–285. https://doi.org/10.17762/ijritcc.v11i2s.6147
Akbari, Y., Al Maadeed, S., Elharrouss, O., Khelifi, F., & Lawgaly, A. (2023). A New Dataset for Forged Smartphone Videos Detection: Description and Analysis. IEEE Access.
Armas Vega, E. A., González Fernández, E., Sandoval Orozco, A. L., & García Villalba, L. J. (2021). Copy-move forgery detection technique based on discrete cosine transform blocks features. Neural Computing and Applications, 33, 4713-4727.
Kadam, K. D., Ahirrao, S., & Kotecha, K. (2022). Efficient approach towards detection and identification of copy move and image splicing forgeries using mask R-CNN with MobileNet V1. Computational Intelligence and Neuroscience, 2022.
Singh, G., & Singh, K. (2019). Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation. Multimedia Tools and Applications, 78, 11527-11562.
Al-Qershi, O. M., & Khoo, B. E. (2018). Evaluation of copy-move forgery detection: datasets and evaluation metrics. Multimedia Tools and Applications, 77, 31807-31833.
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