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Multimedia Tools and Applications

, Volume 78, Issue 19, pp 27109–27126 | Cite as

Video tamper detection based on multi-scale mutual information

  • Wei Wei
  • Xunli FanEmail author
  • Houbing Song
  • Huihui Wang
Article

Abstract

Video frame manipulation has become commonplace with the growing easy access to powerful computing abilities. One of the most common types of video frame tampers is the copy-paste tamper, wherein a region from a video frame is replaced with another region from the same frame. In order to improve the robustness of passive video tampering detection, we propose a content-based video similarity tamper passive blind detection algorithm based on multi-scale normalized mutual information which can implement video frame copy, frame insertion and frame deletion tamper detection. The detail implementation of the proposed algorithm consists of multi-scale content analysis, single-scale content similarity measure, multi-scale content similarity measure, and tampering positioning. Firstly, we get the scales of the visual content of the video frame using Gaussian pyramid transform; Secondly, to measure the similarity of single-scale visual content, we define adjacent normalized mutual information of two frames according to information theory; Thirdly, we construct the multi-scale normalized mutual information descriptors to achieve the multi-scale visual content similarity measure of adjacent two frames using a linear combination. Finally, we use the local outlier isolated factor detection algorithm to detect the position of the video tampering. Experimental results show that the proposed approach can not only detect the video frame tampering position of delete, copy, and insert effectively, but also can detect the tampering of different and homology video encoding formats. We obtain a feature detecting accuracy in excess of 93% and detection rate of 96% across post processing operations, and are able to detect the delete, copy, and insert regions with a high true positive rate and lower false positive rate than the existing time field tamper detection methods.

Keywords

Video tampering Similarity measure Normalized mutual information Gaussian pyramid 

Notes

Acknowledgements

This job is supported by Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No.2013JK1139) and Supported by China Postdoctoral Science Foundation (No.2013M542370) and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20136118120010). And this project is also supported by NSFC Grant (Program No. 11301414 and No.61472318 and No.11226173) and by the Open Program of Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University(600005-Z17X0001).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Wei Wei
    • 1
    • 2
  • Xunli Fan
    • 3
    Email author
  • Houbing Song
    • 4
  • Huihui Wang
    • 5
  1. 1.School of Computer Science and EngineeringXi’an University of TechnologyXi’anChina
  2. 2.Key Laboratory for Computer Vision and Pattern Recognition of Xiamen CityXiamenChina
  3. 3.School of Information Science & TechnologyNorthwest UniversityXi’anChina
  4. 4.Department of Electrical and Computer EngineeringWest Virginia UniversityMontgomeryUSA
  5. 5.Department of EngineeringJacksonville UniversityJacksonvilleUSA

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