Skip to main content
SpringerLink
Log in

Inter-frame passive-blind forgery detection for video shot based on similarity analysis

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Frame insertion, deletion and duplication are common inter-frame tampering operations in digital videos. In this paper, based on similarity analysis, a passive-blind forensics scheme for video shots is proposed to detect inter-frame forgeries. This method is composed of two parts: HSV (Hue-Saturation-Value) color histogram comparison and SURF (Speeded Up Robust Features) feature extraction together with FLANN (Fast Library for Approximate Nearest Neighbors) matching for double-checking. We mainly calculate H-S and S-V color histograms of every frame in a video shot and compare the similarity between histograms to detect and locate tampered frames in the shot. Then we utilize SURF feature extraction and FLANN matching to further confirm the forgery types in the tampered locations. Experimental results demonstrate that the proposed detection method is efficient and accurate in terms of forgery identification and localization. In contrast to other inter-frame forgery detection methods, our scheme can detect three kinds of forgery operations and has its own superiority and applicability as a passive-blind detection method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Abdallah EE, Hamza AB, Bhattacharya P (2007) MPEG video watermarking using tensor singular value decomposition. International Conference Image Analysis and Recognition 4633:772–783

    Article  Google Scholar 

  2. Al-Ayyoub M, AlZu’bi S, Jararweh Y, Shehab MA, Gupta BB (2016) Accelerating 3D medical volume segmentation using GPUs. Multimedia Tools and Applications, pp 1–20, published online Dec. 2016

  3. Al-Qurishi M, Rahman SMM, Hossain MS, Almogren A, Alrubaian M, Alamri A, Al-Rakhami M, Gupta BB (2017) An efficient key agreement protocol for Sybil-precaution in online social network. Future Generation Computer Systems, pp:1–10

  4. Bay H, Tuytelaars T, Van Gool L (2006) SURF: Speeded up robust features. The Ninth European Conference on Computer Vision, Graz, Austria, vol.3951 LNCS, pp 404–417

  5. Chen X, Huang X, Li J, Ma J, Lou W, Wong DS (2015) New algorithms for secure outsourcing of large-scale systems of linear equations. IEEE Transactions on Information and Forensics Security 10(1):69–78

    Article  Google Scholar 

  6. Elsheh E, Hamza AB (2011) Secret sharing approaches for 3D object encryption. Expert Syst Appl 38(11):13906–13911

    Google Scholar 

  7. Gong Y, Xie H, Yu L (2013) Image mosaic based on SURF feature match and similarity transformation. Journal of Computational Information Systems 9(24):9927–9934

    Google Scholar 

  8. Grana C, Vezzani R, Cucchiara R (2007) Enhancing HSV histograms with achromatic points detection for video retrieval. The Sixth ACM International Conference on Image and Video Retrieval, Amsterdam, pp 302–308

    Google Scholar 

  9. Gupta BB, Agrawal DP, Yamaguchi S (2016) Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security. IGI Global

  10. Hou ZJ, Yuan DZ, Huang JS, Wu ZR (2014) Research on the matching algorithm based on SURF. Sensors and Transducers 170(5):281–286

    Google Scholar 

  11. Huang Z, Liu S, Mao X, Chen K, Li J (2017) Insight of the protection for data security under selective opening attacks. Inf Sci S412-413:223–241

    Article  Google Scholar 

  12. Hyun D-K, Lee M-J, Ryu S-J, Lee H-Y, Lee H-K (2013) Forgery detection for surveillance video. In: The Era of Interactive Media. Springer, pp 25–36

  13. Li FG, Huang TQ (2014) Video copy-move forgery detection and localization based on structural similarity. The Third International Conference on Multimedia Technology, Guangzhou, pp 63–76

  14. Li L, Wang X, Zhang W, Yang G, Hu G (2013) Detecting removed object from video with stationary background. In: Digital Forensics and Watermaking. Springer, pp 242–252

  15. Li J, Yu C, Gupta BB, Ren X (2017) Color image watermarking scheme based on quaternion Hadamard transform and Schur decomposition. Multimedia Tools and Applications, pp 1–17, published online Feb. 2017

  16. Lin GS, Chang JF (2012) Detection of frame duplication forgery in videos based on spatial and temporal analysis. International Journal of Pattern Recognition and Artificial Intelligence, 26(7): 1250017(1–18)

  17. Lin CS, Tsay JJ (2013) Passive approach for video forgery detection and localization. In: The Second International Conference on Cyber Security, Cyber Peacefare and Digital Forensic (CyberSec2013), 2013. The Society of Digital Information and Wireless Communication, pp 107–112

  18. Lin CS, Tsay JJ (2014) A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis. Digit Investig 11(2):120–140

    Article  Google Scholar 

  19. Lowe DG (2004) Distinctive image features from scale-invariant key points. Int J Comput Vis 60(2):91–110

    Article  MathSciNet  Google Scholar 

  20. Qin YSG, Zhang X (2009) Exposing digital forgeries in video via motion vectors. J Comput Res Dev 46(Suppl):227–233

    Google Scholar 

  21. Subramanyam AV, Emmanuel S (2012) Video forgery detection using HOG features and compression properties. In: Multimedia Signal Processing (MMSP), IEEE 14th International Workshop on, 17–19 Sept. 2012 2012. pp 89–94. doi:https://doi.org/10.1109/MMSP.2012.6343421

  22. Subramanyam AV, Emmanuel S (2013) Pixel estimation based video forgery detection. In: Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference on, 2013. IEEE, pp 3038–3042

  23. Sun T, Wang W, Jiang X (2012) Exposing video forgeries by detecting MPEG double compression. In: Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference on, 2012. IEEE, pp1389–1392

  24. Wang WH, Farid H (2007) Exposing digital forgeries in video by detecting duplication. Multimedia Security Workshop, Dallas, pp 35–42

    Google Scholar 

  25. Wang JX, Wang YW (2015) Modified SURF applied in remote sensing image stitching. International Journal of Signal Processing, Image Processing and Pattern Recognition 8(8):1–10

    Article  Google Scholar 

  26. Wang Y, Li HX, Wang LK (2014) Similar image retrieval using color histogram in HSV space and SIFT descriptor with FLANN. The Eighth International Conference on Intelligent Systems and Knowledge Engineering, Shenzhen, pp 1085–1093

    Google Scholar 

  27. Wu YX, Jiang XH, Sun TF, Wang W (2014) Exposing video inter-frame forgery based on velocity field consistency. IEEE International Conference on Acoustics, Speech, and Signal Processing, Florence, Italy, pp 2674–2678

  28. Xu B, Wang JW, Liu GJ, Dai YW (2010) Image copy-move forgery detection based on SURF. The Second International Conference on Multimedia Information Networking and Security, Nanjing, pp 889–892

  29. Yang JM, Huang TQ, Su LC (2016) Using similarity analysis to detect frame duplication forgery in videos. Multimedia Tools and Applications 75(4):1793–1811

    Article  Google Scholar 

  30. Zhang ZZ, Hou JJ, Li ZH, Li DD (2016) Inter-frame forgery detection for static-background video based on MVP consistency. Lect Notes Comput Sci 9569:94–106

    Article  Google Scholar 

Download references

Acknowledgments

The project is supported by the Nature Science Foundation of Guangdong Province (2015A030310172), the Science & Technology Plan Projects of Shenzhen (JCYJ20170302145623566).

Author information

Authors and Affiliations

  1. College of Information Engineering, Shenzhen University, Shenzhen, 518060, People’s Republic of China

    Dong-Ning Zhao

  2. Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, People’s Republic of China

    Dong-Ning Zhao

  3. School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Ren-Kui Wang & Zhe-Ming Lu

Authors
  1. Dong-Ning Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ren-Kui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhe-Ming Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhe-Ming Lu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, DN., Wang, RK. & Lu, ZM. Inter-frame passive-blind forgery detection for video shot based on similarity analysis. Multimed Tools Appl 77, 25389–25408 (2018). https://doi.org/10.1007/s11042-018-5791-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5791-1

Keywords

Search

Navigation