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Double H.264 Compression Detection Scheme Based on Prediction Residual of Background Regions

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Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10361))

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Abstract

Detection of double video compression plays an important role in video forensics. However, existing methods rarely focused on H.264 videos and are unreliable to provide detection results for static-background videos with fast moving foregrounds. In this paper, an effective double compression detection scheme based on Prediction Residual of Background Regions (PRBR) is proposed to overcome these limitations. Firstly, the mask of background regions in each frame is obtained by applying Visual Background Extractor (VIBE). VIBE is an efficient and robust background subtraction algorithm, which can distinguish the background and foreground of each frame at pixel level. Then, the PRBR feature is designed to characterize the statistical distribution of average prediction residual within the background mask. After that, the Jesen-Shannon Divergence is introduced to measure the difference between the PRBR features of the adjacent two frames. Finally, a periodic analysis method is applied to the final feature sequence for double H.264 compression detection and estimation of the first Group Of Pictures (GOP). Eighteen standard testing sequences captured by fixed cameras are used to establish the double compression dataset. Experiments demonstrate the proposed scheme can achieve better performance compared the-state-of-art methods.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61572320, 61572321). Corresponding author is Prof. Tanfeng Sun, any comments should be addressed to tfsun@sjtu.edu.cn.

Author information

Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Junjia Zheng, Tanfeng Sun, Xinghao Jiang & Peisong He

  2. National Engineering Lab on Information Content Analysis Techniques, Shanghai, 036001, China

    Tanfeng Sun & Xinghao Jiang

Authors
  1. Junjia Zheng
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  2. Tanfeng Sun
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  3. Xinghao Jiang
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  4. Peisong He
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Corresponding author

Correspondence to Tanfeng Sun .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

Appendix A

Appendix A

Akiyo, bowing, bridge-close, bridge-far, container, deadline, galleon, hall, ice, mother-daughter, news, news-announcer, pamphlet, paris, sign-irene, silent, students, washdc.

YUV address: http://trace.eas.asu.edu/yuv/index.html.

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Zheng, J., Sun, T., Jiang, X., He, P. (2017). Double H.264 Compression Detection Scheme Based on Prediction Residual of Background Regions. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_43

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  • DOI: https://doi.org/10.1007/978-3-319-63309-1_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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