Advertisement

Tampering detection and localization in digital video using temporal difference between adjacent frames of actual and reconstructed video clip

  • Vaishali JoshiEmail author
  • Sanjay Jain
Original Research
  • 8 Downloads

Abstract

The scientific, generalized and automatic methods for detecting forgery became the biggest challenge for scientists and researchers. This problem is true in case of all multimedia contents including audios, graphics and videos. It is harder when one doesn’t know the source and background of video in hand and still expected to establish authenticity of it. However, there are algorithms suggested which can work for such tampering in videos captured with static GOP structure. The problem becomes even more difficult when video is captured using adaptive GOP structure (AGS) scheme in which variable sizes of GOP structures are used to improve coding efficiency and to provide strong temporal scalability. In this paper, an algorithm is proposed which is a passive tampering detection algorithm based on comparison of temporal difference between adjacent video frames of actual video clip and its reconstructed version using intrinsic temporal fingerprints, which can work on videos captured using variable size GOP structures. Firstly, all the video frames are extracted from given video sequence. Then, temporal difference is calculated for each pair of adjacent frames in video’s actual and reconstructed from. Video is reconstructed using frame prediction error. Lastly, the calculated differences are used to find and localize tampering. Our proposed algorithm can effectively classify a video, irrespective of whether captured with fixed or AGSs, as genuine or forged using temporal difference between adjacent video frames in its actual and reconstructed form. Extensive experimental results show that the proposed method achieves promising accuracy in classifying genuine videos and forgeries. The results show that the proposed tampering detection algorithm can detect and precisely locate tampering with an average accuracy of 87.5%.

Keywords

Video forgery MPEG GOP Temporal fingerprints Optical flow Video tampering detection 

References

  1. 1.
    Bestagini P, Fontani M, Milani S, Barni M (2012) An overview on video forensics. In: 20th European signal processing conference (EUSIPCO 2012). IEEE, Bucharest, RomaniaGoogle Scholar
  2. 2.
    Stamm MC, Lin WS, Liu KJR (2012) Temporal forensics and anti-forensics for motion compensated video. IEEE Trans Inf Forensics Secur 7(4):1315–1329.  https://doi.org/10.1109/TIFS.2012.2205568 CrossRefGoogle Scholar
  3. 3.
    Yin P, Yu HH (2001) Classification of video tampering methods and countermeasures using digital watermarking. In: Proceedings of the SPIE, multimedia systems and applications IV, vol 4518, pp 239-246Google Scholar
  4. 4.
    Joshi V, Jain S (2015) Tampering detection in digital video—a review of temporal fingerprints based techniques. In: 2015 2nd international conference on computing for sustainable global development (INDIACom), New Delhi, 2015, pp 1121–1124Google Scholar
  5. 5.
    Wang W, Farid H (2006) Exposing digital forgeries in video by detecting double MPEG compression. In: Proceedings of the ACM multimedia and security workshop, Geneva, Switzerland, 2006, pp 37–47Google Scholar
  6. 6.
    Jia S, Xu Z, Wang H, Feng C, Wang T (2018) Coarse-to-fine copy-move forgery detection for video forensics. IEEE Access 6:25323–25335.  https://doi.org/10.1109/ACCESS.2018.2819624 CrossRefGoogle Scholar
  7. 7.
    Joshi V, Jain S, Bansal C (2018) B-frames: efficiency analysis for digital video tampering detection in videos with variable GOP structure. Int J Comput Sci Eng 6(5):808–815Google Scholar
  8. 8.
    Bestagini P, Milani S, Tagliasacchi M, Tubaro S (2013) Local tampering detection in video sequences. In: MMSP’13, Sept. 30–Oct. 2, 2013, Pula (Sardinia), Italy. 978-1-4799-0125-8/13/$31.00 ©2013 IEEEGoogle Scholar
  9. 9.
    Kingra S, Aggarwal N, Singh RD (2017) Inter-frame forgery detection in H. 264 videos using motion and brightness gradients. Multimed Tools Appl 76:25767.  https://doi.org/10.1007/s11042-017-4762-2 CrossRefGoogle Scholar
  10. 10.
    A library for MATLAB code support. https://sites.google.com/site/santhanarajarunachalam/. Accessed July 2018

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.Department of CSAITM UniversityGwaliorIndia

Personalised recommendations