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Inter-frame forgery detection in H.264 videos using motion and brightness gradients

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Abstract

In the midst of low cost and easy-to-use multimedia editing software, which make it exceedingly simple to tamper with digital content, the domain of digital multimedia forensics has attained considerable significance. This research domain deals with production of tools and techniques that enable authentication of digital evidence prior to its use in various critical and consequential matters, such as politics, criminal investigations, defense planning. This paper presents a forensic scheme for detection of frame-based tampering in digital videos, especially those captured by surveillance cameras. Frame-based tampering, which involves insertion, removal or duplication of frames into or from video sequences, is usually very difficult to detect via simple visual inspection. Such forgeries, however, disturb the temporal correlation among successive frames of the tampered video. These disturbances, when analyzed in an appropriate manner, help reveal the evidence of forgery. The forensic technique presented in this paper relies on objective analysis of prediction residual and optical flow gradients for the detection of frame-based tampering in MPEG-2 and H.264 encoded videos. The proposed technique is also capable of determining the exact location of the forgery in the given video sequence. Results of extensive experimentation in diverse and realistic forensic set-ups show that the proposed technique can detect and locate tampering with an average accuracy of 83% and 80% respectively, regardless of the number of frames inserted, removed or duplicated.

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Notes

  1. Predicted frame is the next subsequent frame predicted using the contents of previous frame.

  2. ffmpeg is a standard audio and video convertor, also adopted as a toolbox in MATLAB.

References

  1. Aghamaleki JA, Behrad A (2016) Inter-frame video forgery detection and localization using intrinsic effects of double compression on quantization errors of video coding. Signal Processing: Image Communication 47:289–302

    Google Scholar 

  2. Arab F, Abdullah SM, Hashim SZ, Manaf AA, Zamani M (2016) A robust video watermarking technique for the tamper detection of surveillance systems. Multimed Tools Appl 75(18):10855–10885.

  3. Barjatya A (2004) Block matching algorithms for motion estimation. IEEE Transactions Evolution Computation 8(3):225–239

    Article  Google Scholar 

  4. Chao J, Jiang X, Sun T (2012) A novel video inter-frame forgery model detection scheme based on optical flow consistency. In: International Workshop on Digital Watermarking, 31 Oct. Springer Berlin: Heidelberg, pp 267–281

  5. Chen W, Shi YQ (2008) Detection of double MPEG compression based on first digit statistics. In: International Workshop on Digital Watermarking, 10 Nov. Springer Berlin: Heidelberg, pp 16–30

  6. Dong Q, Yang G, Zhu N (2012) A MCEA based passive forensics scheme for detecting frame-based video tampering. Digital Investigation 9(2):151–159

    Article  Google Scholar 

  7. Farid H (2006) Exposing digital forgeries in scientific images. In: Proceedings of the 8th workshop on Multimedia and security, ACM, 26 Sept, pp 29–36

  8. 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, 11 Jun. ACM, pp 171–179

  9. Gironi A, Fontani M, Bianchi T, Piva A, Barni M (2014) A video forensic technique for detecting frame deletion and insertion. In: 2014 I.E. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4 May. IEEE, pp. 6226–6230

  10. He P, Sun T, Jiang X, Wang S (2015) Double compression detection in MPEG-4 videos based on block artifact measurement with variation of prediction footprint. In: International Conference on Intelligent Computing, 20 Aug. Springer International Publishing, pp 787–793

  11. Horn BK, Schunck BG (1981) Determining optical flow. Artif Intell 17(1–3):185–203

    Article  Google Scholar 

  12. Jiang X, Wang W, Sun T, Shi YQ, Wang S (2013) Detection of double compression in MPEG-4 videos based on Markov statistics. IEEE Signal processing letters 20(5):447–450

    Article  Google Scholar 

  13. Kobayashi M, Okabe T, Sato Y (2009) Detecting video forgeries based on noise characteristics. In: Pacific-Rim Symposium on Image and Video Technology 2009 Jan 13. Springer Berlin Heidelberg, pp 306–317

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

    Article  Google Scholar 

  15. Liu H, Li S, Bian S (2014) Detecting frame deletion in H. 264 video. In International Conference on Information Security Practice and Experience, 5 May. Springer International Publishing, pp 262–270

  16. Luo W, Wu M, Huang J (2008) MPEG recompression detection based on block artifacts. In: Electronic Imaging, 14 Feb. International Society for Optics and Photonics, pp 68190X–68190X

  17. Milani S, Bestagini P, Tagliasacchi M, Tubaro S (2012) Multiple compression detection for video sequences. In: Multimedia Signal Processing (MMSP), 2012 I.E. 14th International Workshop on, Sep 17. IEEE, pp 112–117

  18. P. U. DIC (2016) Video forgery data at panjab university, chandigarh. Online. Github Repository, https://github.com/navagg/DIC-PU-Videos-Forgery.git, 8

  19. Schneider M, Chang SF (1996) A robust content based digital signature for image authentication. In: Image Processing, 1996. Proceedings International Conference, 16 Sept 1996, vol 3. IEEE, pp 227–230

  20. Shanableh T (2013) Detection of frame deletion for digital video forensics. Digital Investigation 10(4):350–360

    Article  Google Scholar 

  21. Singh VK, Pant P, Tripathi RC (2015) Detection of Frame Duplication Type of Forgery in Digital Video Using Sub-block Based Features. In: International Conference on Digital Forensics and Cyber Crime, 6 Oct. Springer International Publishing, pp 29–38

  22. Stamm MC, Lin WS, Liu KR (2012) Temporal forensics and anti-forensics for motion compensated video. IEEE Transactions on Information Forensics and Security 7(4):1315–1329

    Article  Google Scholar 

  23. Su Y, Xu J (2010) Detection of double-compression in MPEG-2 videos. In: Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on, 22 May. IEEE, pp 1–4

  24. Sun T, Wang W, Jiang X (2012) Exposing video forgeries by detecting MPEG double compression. In: 2012 I.E. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 25 Mar. IEEE, pp 1389–1392

  25. Vazquez-Padin D, Fontani M, Bianchi T, Comesana P, Piva A, Barni M (2012) Detection of video double encoding with GOP size estimation. In: 2012 I.E. International Workshop on Information Forensics and Security (WIFS), 2 Dec. IEEE, pp 151–156

  26. Wang W, Farid H (2006) Exposing digital forgeries in video by detecting double MPEG compression. In Proceedings of the 8th workshop on Multimedia and security, 26 Sept. ACM, pp 37–47

  27. Wang W, Farid H (2007) Exposing digital forgeries in video by detecting duplication. In: Proceedings of the 9th workshop on Multimedia & security, 20 Sept. ACM, pp 35–42

  28. Wang W, Farid H (2009) Exposing digital forgeries in video by detecting double quantization. In: Proceedings of the 11th ACM workshop on Multimedia and security, 7 Sept. ACM, pp 39–48

  29. Wang W, Jiang X, Wang S, Wan M, Sun T (2013) Identifying video forgery process using optical flow. In: International Workshop on Digital Watermarking, 1 Oct. Springer Berlin: Heidelberg, pp 244–257

  30. Wang Q, Li Z, Zhang Z, Ma Q (2014) Video Inter-frame Forgery Identification Based on Optical Flow Consistency. Sensors & Transducers 166(3):229

    Google Scholar 

  31. Zhang Z, Hou J, Li Z, Li D (2015) Inter-frame Forgery Detection for Static-Background Video Based on MVP Consistency. In: International Workshop on Digital Watermarking, 7 Oct. Springer International Publishing, pp 94–106

  32. Zheng L, Sun T, Shi YQ (2014) Inter-frame Video Forgery Detection Based on Block-Wise Brightness Variance Descriptor. In: International Workshop on Digital Watermarking, 1 Oct. Springer International Publishing, pp 18–30

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Authors and Affiliations

  1. UIET, Panjab University, Chandigarh, 160014, India

    Staffy Kingra, Naveen Aggarwal & Raahat Devender Singh

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  1. Staffy Kingra
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  2. Naveen Aggarwal
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  3. Raahat Devender Singh
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Correspondence to Staffy Kingra.

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Kingra, S., Aggarwal, N. & Singh, R.D. Inter-frame forgery detection in H.264 videos using motion and brightness gradients . Multimed Tools Appl 76, 25767–25786 (2017). https://doi.org/10.1007/s11042-017-4762-2

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  • DOI: https://doi.org/10.1007/s11042-017-4762-2

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