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Spatial Video Forgery Detection and Localization using Texture Analysis of Consecutive FramesSADDIQUE, M. , ASGHAR, K. , BAJWA, U. I. , HUSSAIN, M. , HABIB, Z. |
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Author keywords
forensics, image classification, machine learning, multimedia systems
References keywords
detection(25), video(23), image(17), forgery(16), processing(15), multimedia(10), digital(10), signal(9), object(8), pattern(7)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2019-08-31
Volume 19, Issue 3, Year 2019, On page(s): 97 - 108
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.03012
Web of Science Accession Number: 000486574100012
SCOPUS ID: 85072162917
Abstract
Now-a-days, videos can be easily recorded and forged with user-friendly editing tools. These videos can be shared on social networks to make false propaganda. During the process of spatial forgery, the texture and micro-patterns of the frames become inconsistent, which can be observed in the difference of two consecutive frames. Based on this observation, a method has been proposed for detection of forged video segments and localization of forged frames. Employing the Chrominance value of Consecutive frame Difference (CCD) and Discriminative Robust Local Binary Pattern (DRLBP), a new descriptor is introduced to model the inconsistency embedded in the frames due to forgery. Support Vector Machine (SVM) is used to detect whether the pair of consecutive frames is forged. If at least one pair of consecutive frames is detected as forged, the video segment is predicted as forged and the forged frames are localized. Intensive experiments are performed to validate the performance of the method on a combined dataset of videos, which were tampered by copy-move and splicing methods. The detection accuracy on large dataset is 96.68 percent and video accuracy is 98.32 percent. The comparison shows that it outperforms the state-of-the-art methods, even through cross dataset validation. |
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[1] Non-Facial Video Spatiotemporal Forensic Analysis Using Deep Learning Techniques, Premanand Ghadekar, , Vaibhavi Shetty, , Prapti Maheshwari, , Raj Shah, , Anish Shaha, , Vaishnav Sonawane,, Proceedings of Engineering and Technology Innovation, ISSN 2518-833X, Issue , 2023.
Digital Object Identifier: 10.46604/peti.2023.10290 [CrossRef]
[2] A comprehensive survey on passive techniques for digital video forgery detection, Shelke, Nitin Arvind, Kasana, Singara Singh, Multimedia Tools and Applications, ISSN 1380-7501, Issue 4, Volume 80, 2021.
Digital Object Identifier: 10.1007/s11042-020-09974-4 [CrossRef]
[3] A Light Weight Depthwise Separable Layer Optimized CNN Architecture for Object-Based Forgery Detection in Surveillance Videos, Sandhya, , Kashyap, Abhishek, The Computer Journal, ISSN 0010-4620, 2024.
Digital Object Identifier: 10.1093/comjnl/bxae005 [CrossRef]
[4] Classification of Authentic and Tampered Video Using Motion Residual and Parasitic Layers, Saddique, Mubbashar, Asghar, Khurshid, Bajwa, Usama Ijaz, Hussain, Muhammad, Aboalsamh, Hatim A., Habib, Zulfiqar, IEEE Access, ISSN 2169-3536, Issue , 2020.
Digital Object Identifier: 10.1109/ACCESS.2020.2980951 [CrossRef]
[5] Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm, Akhtar, Naheed, Saddique, Mubbashar, Asghar, Khurshid, Bajwa, Usama Ijaz, Hussain, Muhammad, Habib, Zulfiqar, Mathematics, ISSN 2227-7390, Issue 2, Volume 10, 2022.
Digital Object Identifier: 10.3390/math10020168 [CrossRef]
[6] Real time object-based video forgery detection using YOLO (V2), Raskar, Punam Sunil, Shah, Sanjeevani Kiran, Forensic Science International, ISSN 0379-0738, Issue , 2021.
Digital Object Identifier: 10.1016/j.forsciint.2021.110979 [CrossRef]
[7] A comprehensive survey of image and video forgery techniques: variants, challenges, and future directions, Nabi, Syed Tufael, Kumar, Munish, Singh, Paramjeet, Aggarwal, Naveen, Kumar, Krishan, Multimedia Systems, ISSN 0942-4962, Issue 3, Volume 28, 2022.
Digital Object Identifier: 10.1007/s00530-021-00873-8 [CrossRef]
[8] Frame Identification of Object-Based Video Tampering Using Symmetrically Overlapped Motion Residual, Kim, Tae Hyung, Park, Cheol Woo, Eom, Il Kyu, Symmetry, ISSN 2073-8994, Issue 2, Volume 14, 2022.
Digital Object Identifier: 10.3390/sym14020364 [CrossRef]
[9] Spatiotemporal Trident Networks: Detection and Localization of Object Removal Tampering in Video Passive Forensics, Yang, Quanxin, Yu, Dongjin, Zhang, Zhuxi, Yao, Ye, Chen, Linqiang, IEEE Transactions on Circuits and Systems for Video Technology, ISSN 1051-8215, Issue 10, Volume 31, 2021.
Digital Object Identifier: 10.1109/TCSVT.2020.3046240 [CrossRef]
[10] Optical flow and pattern noise-based copy–paste detection in digital videos, Singh, Raahat Devender, Aggarwal, Naveen, Multimedia Systems, ISSN 0942-4962, Issue 3, Volume 27, 2021.
Digital Object Identifier: 10.1007/s00530-020-00749-3 [CrossRef]
[11] The Detection and Classification of Microcalcifications in the Visibility-Enhanced Mammograms Obtained by using the Pixel Assignment-Based Spatial Filter, HEKIM, M., AYDIN YURDUSEV, A., ORAL, C., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 19, 2019.
Digital Object Identifier: 10.4316/AECE.2019.04009 [CrossRef] [Full text]
[12] Inter-frame video forgery detection using UFS-MSRC algorithm and LSTM network, Girish, N., Nandini, C., International Journal of Modeling, Simulation, and Scientific Computing, ISSN 1793-9623, Issue 01, Volume 14, 2023.
Digital Object Identifier: 10.1142/S1793962323410131 [CrossRef]
[13] Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment, Vinolin, V., Sucharitha, M., The Visual Computer, ISSN 0178-2789, Issue 8, Volume 37, 2021.
Digital Object Identifier: 10.1007/s00371-020-01992-5 [CrossRef]
[14] A Novel Moving Object Detection Algorithm Based on Robust Image Feature Threshold Segmentation with Improved Optical Flow Estimation, Ding, Jing, Zhang, Zhen, Yu, Xuexiang, Zhao, Xingwang, Yan, Zhigang, Applied Sciences, ISSN 2076-3417, Issue 8, Volume 13, 2023.
Digital Object Identifier: 10.3390/app13084854 [CrossRef]
[15] A comprehensive survey on state-of-the-art video forgery detection techniques, Mohiuddin, Sk, Malakar, Samir, Kumar, Munish, Sarkar, Ram, Multimedia Tools and Applications, ISSN 1380-7501, Issue 22, Volume 82, 2023.
Digital Object Identifier: 10.1007/s11042-023-14870-8 [CrossRef]
[16] Image Forgery Detection Using Noise and Edge Weighted Local Texture Features, ASGHAR, K., SADDIQUE, M., HUSSAIN, M., BEBIS, G., HABIB, Z., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 22, 2022.
Digital Object Identifier: 10.4316/AECE.2022.01007 [CrossRef] [Full text]
[17] Duplicate Frame Video Forgery Detection Using Siamese-based RNN, Munawar, Maryam, Noreen, Iram, Intelligent Automation & Soft Computing, ISSN 1079-8587, Issue 3, Volume 29, 2021.
Digital Object Identifier: 10.32604/iasc.2021.018854 [CrossRef]
[18] Object based Forgery Detection in Surveillance Videos using Optimized CNN, Sandhya, , Kashyap, Abhishek, 2022 8th International Conference on Signal Processing and Communication (ICSC), ISBN 978-1-6654-5430-8, 2022.
Digital Object Identifier: 10.1109/ICSC56524.2022.10009279 [CrossRef]
[19] A Comparative Study of Deepfake Video Detection Method, Ramadhani, Kurniawan Nur, Munir, Rinaldi, 2020 3rd International Conference on Information and Communications Technology (ICOIACT), ISBN 978-1-7281-7356-6, 2020.
Digital Object Identifier: 10.1109/ICOIACT50329.2020.9331963 [CrossRef]
[20] Digital video tampering detection using texture with compressed passive technic, Vistro, Daniel Mago, Rehman, Attique Ur, Usman, Muhammad, Abbas, Sana, WOMEN IN PHYSICS: 7th IUPAP International Conference on Women in Physics, ISBN , Issue , 2024.
Digital Object Identifier: 10.1063/5.0181757 [CrossRef]
[21] Video Forgery Detection using CNN, Koshy, Litty, S, Ajay, Paul, Akhil, V, Hariharan, Basheer, Ashil, 2021 Smart Technologies, Communication and Robotics (STCR), ISBN 978-1-6654-1806-5, 2021.
Digital Object Identifier: 10.1109/STCR51658.2021.9588860 [CrossRef]
[22] Combating Online Misinformation Videos: Characterization, Detection, and Future Directions, Bu, Yuyan, Sheng, Qiang, Cao, Juan, Qi, Peng, Wang, Danding, Li, Jintao, Proceedings of the 31st ACM International Conference on Multimedia, ISBN 9798400701085, 2023.
Digital Object Identifier: 10.1145/3581783.3612426 [CrossRef]
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