Digital Video Tampered Inter-frame Multi-scale Content Similarity Detection Method

  • Lan WuEmail author
  • Xiao-qiang Wu
  • Chunyou Zhang
  • Hong-yan Shi
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 302)


With the popularity of the Internet and the increasing power of video editing software, digital video can easily be tampered with. The detection of the authenticity and integrity of digital video is very important. A video tampering detection method based on multi-scale normalized mutual information is proposed. Firstly, the mutual information is introduced into video tamper detection and the normalized mutual information content of the video frames is extracted. Then, based on the “scale invariance” feature of human vision, the mutual information between frames is analyzed from a multi-scale perspective. The multi-scale normalized mutual information is used to characterize the similarity of content between video frames. Finally, the LOF algorithm is used to calculate the degree of abnormality of the similarity coefficient sequence to achieve three kinds of tampering detection in the time domain: deletion, insertion, and replication. Experimental results show that the proposed method can effectively detect tampered video.


Video tampering Content continuity Multi-scale Content anomaly 



Inner Mongolia National University Research Project (NMDYB1729).


  1. 1.
    Amanipour, V., Ghaemmaghami, S.: Video-tampering detection and content reconstruction via self-embedding. IEEE Trans. Instrum. Meas. 99, 1–11 (2017)Google Scholar
  2. 2.
    Hu, W.C., Chen, W.H., Huang, D.Y., et al.: Effective image forgery detection of tampered foreground or background image based on image watermarking and alpha mattes. Multimed. Tools Appl. 75(6), 3495–3516 (2016)CrossRefGoogle Scholar
  3. 3.
    Wu, M.L., Fahn, C.S., Chen, Y.F.: Image-format-independent tampered image detection based on overlapping concurrent directional patterns and neural networks. Appl. Intell. 47(2), 347–361 (2017)CrossRefGoogle Scholar
  4. 4.
    Lin, J., Huang, T., Lai, Y., et al.: Detection of continuously and repeated copy-move forgery to single frame in videos by quantized DCT coefficients. J. Comput. Appl. (2016)Google Scholar
  5. 5.
    Fallahpour, M., Shirmohammadi, S., Semsarzadeh, M., et al.: Tampering detection in compressed digital video using watermarking. IEEE Trans. Instrum. Meas. 63(5), 1057–1072 (2014)CrossRefGoogle Scholar
  6. 6.
    Tang, Z., Wang, S., Zhang, X., et al.: Structural feature-based image hashing and similarity metric for tampering detection. Fundamenta Informaticae 106(1), 75–91 (2011)MathSciNetGoogle Scholar
  7. 7.
    Huang, D.Y., Chen, C.H., Chen, T.Y., et al.: Rapid detection of camera tampering and abnormal disturbance for video surveillance system. J. Vis. Commun. Image Represent. 25(8), 1865–1877 (2014)CrossRefGoogle Scholar
  8. 8.
    Sitara, K., Mehtre, B.M.: Digital video tampering detection: An overview of passive techniques. Digit. Invest. 18(8), 8–22 (2016)CrossRefGoogle Scholar
  9. 9.
    Aghamaleki, J.A., Behrad, A.: Malicious inter-frame video tampering detection in MPEG videos using time and spatial domain analysis of quantization effects. Multimed. Tools Appl. 76(20), 1–27 (2016)Google Scholar
  10. 10.
  11. 11.
    Zhang, X., Huang, T., Lin, J., et al.: Video tamper detection method based on nonnegative tensor factorization. Chin. J. Netw. Inf. Secur. 3(6), 1–8 (2017)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Lan Wu
    • 1
    Email author
  • Xiao-qiang Wu
    • 1
  • Chunyou Zhang
    • 1
  • Hong-yan Shi
    • 1
  1. 1.College of Mechanical EngineeringInner Mongolia University for the NationalitiesTongliaoChina

Personalised recommendations