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针对视频运动补偿帧率提升篡改的主动混噪取证算法

李然 梅腊腊 邬长安 朱秀昌

李然, 梅腊腊, 邬长安, 朱秀昌. 针对视频运动补偿帧率提升篡改的主动混噪取证算法[J]. 电子与信息学报, 2018, 40(3): 713-720. doi: 10.11999/JEIT170502
引用本文: 李然, 梅腊腊, 邬长安, 朱秀昌. 针对视频运动补偿帧率提升篡改的主动混噪取证算法[J]. 电子与信息学报, 2018, 40(3): 713-720. doi: 10.11999/JEIT170502
LI Ran, MEI Lala, WU Chang'an, ZHU Xiuchang. Active Noised-mixed Forensics Algorithm for Tampering of Video Motion-compensated Frame Rate Up-conversion[J]. Journal of Electronics & Information Technology, 2018, 40(3): 713-720. doi: 10.11999/JEIT170502
Citation: LI Ran, MEI Lala, WU Chang'an, ZHU Xiuchang. Active Noised-mixed Forensics Algorithm for Tampering of Video Motion-compensated Frame Rate Up-conversion[J]. Journal of Electronics & Information Technology, 2018, 40(3): 713-720. doi: 10.11999/JEIT170502

针对视频运动补偿帧率提升篡改的主动混噪取证算法

doi: 10.11999/JEIT170502
基金项目: 

国家自然科学基金(61501393)

Active Noised-mixed Forensics Algorithm for Tampering of Video Motion-compensated Frame Rate Up-conversion

Funds: 

The National Natural Science Foundation of China (61501393)

  • 摘要: 运动补偿帧率提升(MC-FRUC)是常见的视频时域篡改手段。现有方法依靠被动分析视频统计特征发现MC-FRUC篡改,然而,视频统计特性的非平稳性影响了取证性能的稳定性。该文提出一种主动混噪取证算法,通过预先混入统计特性已知的高斯白噪声,提高MC-FRUC取证的准确度。首先,利用伪随机序列生成高斯白噪声,加入原始视频序列。接着,由小波系数的绝对中位差预测各视频帧中混入高斯噪声的标准差。最后,检测高斯噪声标准差的时域变化周期性,通过硬阈值判决,自动甄别MC-FRUC篡改。实验结果表明,针对不同的MC-FRUC伪造方法,提出算法均表现出良好的取证性能,尤其是当采用去噪、压缩等操作后处理视频后,提出算法仍能确保较高的检测准确度。
  • 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.
    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.
    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.
    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.
    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.
    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.
    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
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
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出版历程
  • 收稿日期:  2017-05-24
  • 修回日期:  2017-11-10
  • 刊出日期:  2018-03-19

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