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Detecting Double H.264 Compression Based on Analyzing Prediction Residual Distribution

  • S. Chen
  • T. F. Sun
  • X. H. JiangEmail author
  • P. S. He
  • S. L. Wang
  • Y. Q. Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10082)

Abstract

Detecting double video compression has become an important issue in video forensics. A novel double H.264 compression detection scheme based on Prediction Residual Distribution (PRED) analysis is proposed in the paper. The proposed scheme can be applied to detect double H.264 compression with non-aligned GOP structures. For each frame of a given video, the prediction residual is first calculated and the average value of the prediction residual in each non-overlapping 4 × 4 block is recorded to reduce the influence of the noise. Then the PRED feature is represented by the distribution of the average prediction residual in each frames. After that, the Jensen-Shannon Divergence (JSD) is introduced to measure the difference between the PRED features of adjacent two frames. Finally, a Periodic Analysis (PA) method is applied to the final feature sequence to detect double H.264 compression and to estimate the first GOP size. Fourteen public YUV sequences are adopted for evaluation. Experiments have demonstrated that the proposed scheme can achieve better performance than the state-of-the-art method investigated.

Keywords

Double compression detection Prediction residual distribution Non-aligned GOP structure First GOP estimation 

Notes

Acknowledgment

This work was supported by the National Natural Science Foundation of China (Nos. 61572320, 61572321, 61272249, 61272439, 61271319). Corresponding author is X.H. Jiang, any comments should be addressed to xhjiang@sjtu.edu.cn.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • S. Chen
    • 1
  • T. F. Sun
    • 1
  • X. H. Jiang
    • 1
    Email author
  • P. S. He
    • 1
  • S. L. Wang
    • 1
  • Y. Q. Shi
    • 2
  1. 1.EIEEShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.ECENew Jersey Institute of TechnologyNewarkUSA

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