International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.11, No.12, Dec. 2019
Three-dimensional Region Forgery Detection and Localization in Videos
Full Text (PDF, 1288KB), PP.1-13
Nowadays, with the extensive use of cameras in many areas of life, every day millions of videos are uploaded on the internet. In addition, with rapidly developing video editing software applications, it has become easier to forge any video. These software applications have made it challenging to detect forged videos, especially with forged videos have duplication of three-dimensional (3-D) regions. Recently, there has been increased interest in detecting forged videos, but there are very limited studies to detect forged videos which were duplicated 3-D regions. So, our research focused on this weakness and proposed a new method, which can be used for detecting and locating 3-D duplicated regions in videos based on the phase-correlation of 3-D regions residual more efficiently. To evaluate the efficiency of the proposed method, we experimented with two realistic datasets VFDD-3D and REWIND-3D. The results of the experiments proved that the proposed method is efficient and robust for detecting small 3-D regions duplication and frame sequences duplication, especially localization of duplication forgery in videos has shown impressive results.
Cite This Paper
Xuan Hau Nguyen, Yongjian Hu, Muhmmad Ahmad Amin, Khan Gohar Hayat, Van Thinh Le, Dinh Tu Truong, " Three-dimensional Region Forgery Detection and Localization in Videos", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.12, pp. 1-13, 2019.DOI: 10.5815/ijigsp.2019.12.01
Sitara, K. and B.M. Mehtre, Digital video tampering detection: An overview of passive techniques. Digital Investigation, 2016. 18: p. 8-22.
Bestagini, P., et al. Local tampering detection in video sequences. in 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP). 2013. IEEE.
Al Hamidi, S., VFDD (Video Forgery Detection Database) Version 1.0. http://sites.scut.edu.cn/misip/main.psp, 2017.
Qadir, G., S. Yahaya, and A.T. Ho, Surrey university library for forensic analysis (SULFA) of video content. 2012.
Wang, Q., et al., Video inter-frame forgery identification based on consistency of correlation coefficients of gray values. Journal of Computer and Communications, 2014. 2(04): p. 51.
Wang, W. and H. Farid. Exposing digital forgeries in video by detecting duplication. in Proceedings of the 9th workshop on Multimedia & security. 2007. ACM.
Yang, J., T. Huang, and L. Su, using similarity analysis to detect frame duplication forgery in videos. Multimedia Tools and Applications, 2016. 75(4): p. 1793-1811.
Singh, G. and K. Singh, Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation. Multimedia Tools and Applications, 2018: p. 1-36.
Chao, J., X. Jiang, and T. Sun. A novel video inter-frame forgery model detection scheme based on optical flow consistency. in International Workshop on Digital Watermarking. 2012. Springer.
Jia, S., et al., Coarse-to-fine copy-move forgery detection for video forensics. IEEE Access, 2018. 6: p. 25323-25335.
Liu, Y. and T. Huang, Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis. Multimedia Systems, 2017. 23(2): p. 223-238.
Wang, W. and H. Farid. Exposing digital forgeries in video by detecting double quantization. in Proceedings of the 11th ACM workshop on Multimedia and security. 2009. ACM.
Subramanyam, A. and S. Emmanuel. Pixel estimation-based video forgery detection. in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. 2013. IEEE.
Ravi, H., et al. Compression noise-based video forgery detection. in 2014 IEEE International Conference on Image Processing (ICIP). 2014. IEEE.
Huang, Z., F. Huang, and J. Huang. Detection of double compression with the same bit rate in MPEG-2 videos. in 2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP). 2014. IEEE.
Li, L., et al. Detecting removed object from video with stationary background. in International Workshop on Digital Watermarking. 2012. Springer.
Kobayashi, M., T. Okabe, and Y. Sato, detecting forgery from static-scene video based on inconsistency in noise level functions. IEEE Transactions on Information Forensics and Security, 2010. 5(4): p. 883-892.
Pandey, R.C., S.K. Singh, and K. Shukla. Passive copy-move forgery detection in videos. in 2014 International Conference on Computer and Communication Technology (ICCCT). 2014. IEEE.
Subramanyam, A. and S. Emmanuel. Video forgery detection using HOG features and compression properties. in 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP). 2012. IEEE.
Bestagini, P., et al., REWIND Video: copy -move forgeries dataset. https://sites.google.com/site/rewindpolimi/downloads/datasets/video-copy-move-forgeries-dataset. 2012.
Al-Sanjary, O.I., A.A. Ahmed, and G. Sulong, Development of a video tampering dataset for forensic investigation. Forensic science international, 2016. 266: p. 565-572.