Skip to main content

Advertisement

Springer Nature Link
Log in

Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation

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

Abstract

In this paper, we present a passive blind scheme consisting of two different algorithms to detect frame and region duplication forgeries in videos. We have examined the video frame duplication forgery in three different forms such as duplication of a sequence of consecutive video frames at long continue running position, duplication of many such sequences having different lengths at many different locations and duplication from other videos having different and same dimensions which can raise a serious problem in the real world scenario. The algorithm I of proposed scheme has detected these three different forms of copy-moved frame duplication forgery in videos by obtaining the mean features of each video frame for evaluating the correlation between sequences. In this paper, we have also analysed forged regular and irregular region within same frame at different locations and from other frame to one or more sequences of consecutive frames of the same video at same locations. It creates a challenge to detect this copy-move forgery due to slightly change in pixel intensity values in the duplicated region and providing high correlation as authentic region. The algorithm II of proposed scheme has detected these copy-moved region duplication forgeries in videos by locating the position of error with threshold process in order to calculate the similarities between regions of two frames or within affected frame. In this paper, the experimental results show the higher detection accuracy and execution time efficiency of proposed scheme than the latest algorithms with satisfactory performance.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

References

  1. Bestagini P, Milani S,Tagliasacchi M, Tubaro S (2013) Local tampering detection in video sequences, IEEE MMSP, pp. 488–493

  2. Bozkurt I, Bozkurt MH, Ulutas G (2017) A new video forgery detection approach based on forgery line. Turk J Electr Eng Comput Sci 25:4558–4574

    Article  Google Scholar 

  3. Cabeen K, Gent P Image Compression and the Discrete Cosine Transform. https://www.math.cuhk.edu.hk/~lmlui/dct.pdf

  4. Cao Y, Gao T, Fan L, Yang Q (2012) A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 214:33–43

    Article  Google Scholar 

  5. Chetty G, Biswas M, Singh R (2010) Digital video tamper detection based on multimodal fusion of residue features. In: Proceeding of IEEE International Conference on Network and system security, pp. 606–613

  6. Feng C, Xu Z, Jia S, Zhang W, Xu Y (2016) Motion-Adaptive Frame Deletion Detection for Digital Video Forensics. IEEE Trans on Circuits and Systems for Video Tech. https://doi.org/10.1109/TCSVT.2016.2593612

    Article  Google Scholar 

  7. Feng C, Xu Z, Zhang W, Xu Y (2014) Automatic location of frame deletion point for digital video forensics, In proceedings of the 2nd ACM workshop on Information hiding and multimedia security, pp. 171–179

  8. Goodwin J, Chetty G (2011) Blind video tamper detection based on fusion of source features. In Proceeding of IEEE International Conference on Digital image computing techniques and applications, pp.608–613

  9. Ke Y, Sukthankar R, Huston L (2004) An efficient parts-based near-duplicate and sub-image retrieval system. In proceedings of the 12th annual ACM international conference on Multimedia, pp. 869–876

  10. Lin GS, Chang JF (2012) Detection of frame duplication forgery in videos based on spatial and temporal analysis. Int J Pattern Recognit Artif Intell 26(07):1–18

    Article  MathSciNet  Google Scholar 

  11. Liu Y, Huang T (2017) Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis. Multimedia Systems 23:223–238

    Article  Google Scholar 

  12. Poisel R, Tjoa S (2011) Forensics Investigations of Multimedia Data: A Review of the State-of-the-Art. Proceeding of IEEE International Conference on IT Security Incident Management and IT Forensics:48–61. https://doi.org/10.1109/IMF.2011.14

  13. Pun CM, Yuan XC, Bi XL (2015) Image forgery detection using adaptive over segmentation and feature point matching. IEEE Transactions on Information Forensics and Security 10(8):1705–1716

    Article  Google Scholar 

  14. Ravi H, Subramanyam AV, Gupta G, Kumar BA (2014) Compression Noise Based Video Forgery Detection, In: Proc. of IEEE International Conference on Image Processing, Paris, France, pp. 5352–5356

  15. Sanjary OSA, Sulong G (2015) Detection of Video Forgery: A Review of Literature. Journal of Theoretical and Applied Information Technology 74(2):207–220

    Google Scholar 

  16. Shanableh T (2013) Detection of frame deletion for digital video forensics. Digit Investig 10(4):350–360

    Article  Google Scholar 

  17. Sharma S, Dhavale SV (2016) A review of passive forensic techniques for detection of copy-move attacks on digital videos. In proceeding of IEEE International Conference on Advanced Computing and Communication Systems

  18. Singh RD, Aggarwal N (2017) Video content authentication techniques: a comprehensive survey. Multimedia Systems 2. https://doi.org/10.1007/s00530-017-0538-9

    Article  Google Scholar 

  19. Singh VK, Pant P, Tripathi RC (2015) Detection of Frame Duplication Type of Forgeryin Digital Video Using Sub-block Based Features. Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, LNICST 157:29–38. https://doi.org/10.1007/978-3-319-25512-5_3

    Article  Google Scholar 

  20. Sitara K, Mehtre BM (2016) Digital video tampering detection: An overview of passive techniques. Digit Investig 18:8–22

    Article  Google Scholar 

  21. Su L, Li C (2017) A novel passive forgery detection algorithm for video region duplication. Multidimensional Systems and Signal Processing, Springer Nature. https://doi.org/10.1007/s11045-017-0496-6

    Article  MathSciNet  Google Scholar 

  22. Su Y, Zhang J, Liu J (2009) Exposing Digital Video Forgery by Detecting Motion-compensated Edge Artifact, IEEE International Conference on Computational intelligence and software engg

  23. Subramanyam AV, Emmanuel S (2012) Video forgery detection using HOG features and compression properties. In: Proc. of IEEE 14th International Workshop on Multimedia Signal Processing, pp. 89–94

  24. Surrey University Library for Forensic Analysis (SULFA) Data available: http://sulfa.cs.surrey.ac.uk/videos.php

  25. Thajeel SA, Sulong GB (2013) State of the art of copy-move forgery detection techniques: a review. IJCSI International Journal of Computer Science Issues 10(2):6

    Google Scholar 

  26. Ulutas G, Ustubioglu B, Ulutas M, Nabiyev V (2017) Frame duplication/mirroring detection method with binary features. IET Image Process 11(5):333–342

    Article  Google Scholar 

  27. Vahora S, Chauhan N, Prajapati N (2012) A Robust Method for Moving Object Detection Using Modified Statistical Mean Method. International Journal of Advanced Info Tech 2(1):65–73

    Article  Google Scholar 

  28. Wahab AWA, Bagiwa MA, Idris MYI, Khan S, Razak Z, Ariffin MRK (2014) Passive Video Forgery Detection Techniques: A Survey. In proceeding of IEEE International Conference on Information Assurance and Security, pp. 29–34

  29. Wang W, Farid H (2007) Exposing digital forgeries in video by detecting duplication. In: Proceedings of 9th workshop on Multimedia & Security, pp. 35–42

  30. Wang W, Farid H (2007) Exposing digital forgeries in interlaced and de-interlaced video. Inf Forensics Secur IEEE Trans 2(3):438–449

    Article  Google Scholar 

  31. Wang W, Farid H (2007) Exposing digital forgeries in video by detecting duplication. In: Proceedings of the 9th workshop on multimedia & security, ACM, pp. 35–42

  32. Wang W, Jiang X, Wang S, Wan M, Sun T (2014) Identifying video forgery process using optical flow. In: Shi Y, Kim HJ, Pérez-González F (eds) Digital-Forensics and Watermarking. IWDW 2013. Lecture Notes in Computer Science, vol. 8389. Springer, Berlin, Heidelberg, pp. 244–257. https://doi.org/10.1007/978-3-662-43886-2_18

    Google Scholar 

  33. Wang S-H, Zhang Y, Li Y-J, Jia W-J, Liu F-Y, Yang M-M, Zhang Y-D (2018) Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimed Tools Appl 77:10393–10417

    Article  Google Scholar 

  34. Wang S, Zhang Y, Yang X, Sun P, Dong Z, Lin A, Yuan T-F (2015) Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy. Entropy 17(12):8278–8296

    Article  Google Scholar 

  35. Wu Y, Jiang X, Sun T, Wang W (2014) Exposing Video Inter-Frame Forgery Based On Velocity Field Consistency, In: Proceeding of IEEE International Conference on Acoustic, Speech and Signal Processing, pp. 2674–2678

  36. Xu ZJ, Wang ZZ, Lu Q (2011) Research on Image Watermarking Algorithm based on DCT. Procedia Environ Sci 10:1129–1135

    Article  Google Scholar 

  37. Yang J, Huang T, Su L (2016) Using similarity analysis to detect frame duplication forgery in videos. Multimed Tools Appl 75:1793–1811

    Article  Google Scholar 

  38. Zhang Y-D, Hou X-X, Chen Y, Chen H, Yang M, Yang J, Wang S-H (2017) Voxelwise detection of cerebral microbleed in CADASIL patients by leaky rectified linear unit and early stopping. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-4383-9

    Article  Google Scholar 

  39. Zhang J, Su Y, Zhang M (2009) Exposing digital video forgery by ghost shadow artefact. Proceedings of the first ACM workshop on multimedia in forensics, In, pp 49–54

    Google Scholar 

  40. Zheng L, Wang S, Tian Q (2014) Coupled Binary Embedding for Large-Scale Image Retrieval. IEEE Trans Image Process 23(8):3368–3380

    Article  MathSciNet  Google Scholar 

  41. Zheng L, Yang Y, Tian Q (2018) SIFT Meets CNN: A Decade Survey of Instance Retrieval. IEEE Trans Pattern Anal Mach Intell 40(5):1224–1244

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Gurvinder Singh & Kulbir Singh

Authors
  1. Gurvinder Singh
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Kulbir Singh
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Gurvinder Singh.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, G., Singh, K. Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation. Multimed Tools Appl 78, 11527–11562 (2019). https://doi.org/10.1007/s11042-018-6585-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6585-1

Keywords