|
|
|
| Active Noised-mixed Forensics Algorithm for Tampering of Video Motion-compensated Frame Rate Up-conversion |
| LI Ran①② MEI Lala① WU Chang'an① ZHU Xiuchang② |
①(School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China)
②(College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China) |
|
|
|
|
Abstract Motion-Compensated Frame Rate Up-Conversion (MC-FRUC) is one of the common temporal-domain tampering methods of video. The existing methods recognize MC-FRUC tampering by passively analyzing statistical characteristics of video; however, the non-stationarity in statistics of video affects the stability of forensics. This paper proposes an active noise-mixed forensics algorithm. First, white Gaussian noises are produced using a pseudorandom sequence, and these noises are added into the original video sequence. Second, based on the median absolute deviation of wavelet coefficients, the standard deviation of mixed Gaussian noises in each video frame is estimated. Last, the periodicity of standard deviation varying in time domain is detected, and MC-FRUC tampering with a hard-thresholding operation is automatically identified. Experimental results indicate that the proposed algorithm presents better performance of forensics for various MC-FRUC methods, and can still ensure high detection accuracy especially after videos are denoised or compressed.
|
|
Received: 24 May 2017
Published: 11 December 2017
|
|
|
| Fund:The National Natural Science Foundation of China (61501393) |
|
Corresponding Authors:
LI Ran
E-mail: liran358@163.com
|
|
|
|
| [1] |
TSAI T H, SHI A T, and HUANG K T. Accurate frame rate up-conversion for advanced visual quality[J]. IEEE Transactions on Broadcasting, 2016, 62(2): 426-435. doi: 10.1109/TBC.2016.2550764.
|
| [2] |
BIAN S, LUO W, and HUANG J. Exposing fake bit rate videos and estimating original bit rates[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(12): 2144-2154. doi: 10.1109/TCSVT.2014.2334031.
|
| [3] |
BIAN S, LUO W, and HUANG J. Detecting video frame-rate up-conversion based on periodic properties of inter-frame similarity[J]. Multimedia Tools and Applications, 2014, 72(1): 437-451. doi: 10.1007/s11042-013-1364-5.
|
| [4] |
WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: From error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612. doi: 10.1109/TIP.2003.819861.
|
| [5] |
YANG J, HUANG T, and SU L. Using similarity analysis to detect frame duplication forgery in videos[J]. Multimedia Tools and Applications, 2016, 75(4): 1793-1811. doi: 10.1007/ s11042-014-2374-7.
|
| [6] |
CHOI D, SONG W, CHOI H, et al. MAP-based motion refinement algorithm for block-based motion-compensated frame interpolation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 26(10): 1789-1804. doi: 10.1109/TCSVT.2015.2473275.
|
| [7] |
BESTAGINI P, BATTALIA S, MILANI S, et al. Detection of temporal interpolation in video sequences[C]. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 2013: 3033-3037. doi: 10.1109/ICASSP.2013.6638215
|
| [8] |
YAO Y, YANG G, SUN X, et al. Detecting video frame-rate up-conversion based on periodic properties of edge- intensity[J]. Journal of Information Security & Applications, 2016, 26(3): 8399-8421. doi: 10.1007/S11042-016-3468-1.
|
| [9] |
XIA M, YANG G, LI L, et al. Detecting video frame rate up-conversion based on frame-level analysis of average texture variation[J]. Multimedia Tools & Applications, 2017, 76(6): 8399-8421. doi: 10.1007/S11042-016-3468-1.
|
| [10] |
DING X, YANG G, LI R, et al. Identification of motion- compensated frame rate up-conversion based on residual signal[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, pp(99): 1-1. doi: 10.1109/TCSVT. 2017.2676162.
|
| [11] |
DE H G, BIEZEN P W A C, HUIJGEN H, et al. True-motion estimation with 3-D recursive search block matching[J]. IEEE Transactions on Circuits and Systems for Video Technology, 1993, 3(5): 368-379. doi: 10.1109/76.246088.
|
| [12] |
YOO D G, KANG S J, and KIM Y H. Direction-select motion estimation for motion-compensated frame rate up- conversion[J]. Journal of Display Technology, 2013, 9(10): 840-850. doi: 10.1109/JDT.2013.2263374.
|
| [13] |
LIU H, XIONG R, ZHAO D, et al. Multiple hypotheses bayesian frame rate up-conversion by adaptive fusion of motion-compensated interpolations[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2012, 22(8): 1188-1198. doi: 10.1109/TCSVT.2012.2197081.
|
| [14] |
JEONG S G, LEE C, and KIM C S. Motion-compensated frame interpolation based on multihypothesis motion estimation and texture optimization[J]. IEEE Transactions on Image Processing, 2013, 22(11): 4497-4509. doi: 10.1109/ TIP.2013.2274731.
|
| [15] |
LI R, LIU Z, ZHANG Y, et al. Noise-level estimation based detection of motion-compensated frame interpolation in video sequences[J]. Multimedia Tools & Applications, 2017, 76(10): 1-26. doi: 10.1007/s11042-016-4268-3.
|
| [16] |
LEIGH A, WONG A, CLAUSI D A, et al. Comprehensive analysis on the effects of noise estimation strategies on image noise artifact suppression performance[C]. 2011 IEEE International Symposium on Multimedia, Washington, DC, USA, 2011: 97-104.
|
|
|
|
