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Detection and localization of multiple inter-frame forgeries in digital videos

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Abstract

Sanctity and integrity of digital videos are crucial for the diverse real-world applications. It has significant social and legal implications. The technological advancements are posing new challenges as the video processing software that are typically designed to enhance the visual content, can adversely spawn unauthentic and malicious data that can be potentially hazardous. Robust algorithms are therefore needed to counter the deleterious effects. In this paper, we propose a passive-blind approach to detect and localize multiple kinds of inter-frame forgeries in digital videos like frame insertion, deletion and duplication. The forensic artefacts are designed based on correlation inconsistencies between the histogram-similarity patterns of the adjacent texture-feature encoded video frames. For the empirical evaluation, the algorithm uses texture features such as Histogram of Oriented Gradients (HoG), uniform and rotation invariant Local Binary Pattern (LBP). A customized dataset of 1370 tampered videos is created using the benchmark SULFA dataset due to lack of standard video dataset with inter-frame forgeries. A supervised SVM classifier is trained to detect video tampering where extensive analysis based on different histogram-similarity metrics is carried out with the proposed approach that exhibits an overall accuracy 99%. Further, the proposed method localizes the position of tampered frames in the video. It highlights forged frames using Chebyshev's inequality in case of frame insertion and deletion attacks. A comparative analysis with state-of-the-art methods is also presented that exhibits good efficacy of the proposed approach.

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Data availability

The results of this manuscript are prepared through testing and validation on self-generated datasets that can be shared on reasonable request.

References

  1. Johnston P, Elyan E (2019) A review of digital video tampering: From simple editing to full synthesis. Digit Investig 29:67–81

    Article  Google Scholar 

  2. Hurrah NN, Parah SA, Loan NA, Sheikh JA, Elhoseny M, Muhammad K (2019) Dual watermarking framework for privacy protection and content authentication of multimedia. Futur Gener Comput Syst 94:654–673

    Article  Google Scholar 

  3. Bozkurt I, Bozkurt MH, Ulutaş G (2017) A new video forgery detection approach based on forgery line. Turk J Electr Eng Comput Sci 25(6):4558–4574

    Article  Google Scholar 

  4. Hyun DK, Ryu SJ, Lee HY, Lee HK (2013) Detection of upscale-crop and partial manipulation in surveillance video based on sensor pattern noise. Sensors (Switzerland) 13(9):12605–12631

    Article  ADS  Google Scholar 

  5. Wang W, Farid H (2009) Exposing digital forgeries in video by detecting double quantization. MMandSec’09 - Proceedings of the 11th ACM Multimedia Security Workshop, pp. 39–47

  6. Singh RD, Aggarwal N (2017) Detection of upscale-crop and splicing for digital video authentication. Digit Investig 21:31–52

    Article  Google Scholar 

  7. Li L, **a Z, Hadid A, Jiang X, Zhang H, Feng X (2019) Replayed video attack detection based on motion blur analysis. IEEE Trans Inf Forensics Secur 14(9):2246–2261

    Article  Google Scholar 

  8. Zhang Y, Dubey RK, Hua G, Thing VLL (2019) Face Spoofing Video Detection Using Spatio-Temporal Statistical Binary Pattern. IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2018-Octob, no. October, pp. 309–314

  9. Schaber P, Dong S, Guthier B, Kopf S, Effelsberg W (2015) Modeling temporal effects in re-captured video. MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, pp. 1279–1282

  10. Esmaeili MM, Fatourechi M, Ward RK (2011) A robust and fast video copy detection system using content-based fingerprinting. IEEE Trans Inf Forensics Secur 6(1):213–226

    Article  Google Scholar 

  11. Lameri S, Bondi L, Bestagini P, Tubaro S (2018) Near-duplicate video detection exploiting noise residual traces. Proceedings - International Conference on Image Processing, ICIP. 2017-Septe, pp. 1497–1501

  12. Yang X, Li Y, Lyu S (2019) Exposing Deep Fakes Using Inconsistent Head Poses. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2019-May, pp. 8261–8265

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

    Article  Google Scholar 

  14. Singh RD, Aggarwal N (2018) Video content authentication techniques: a comprehensive survey. Multimedia Syst 24(2):211–240

    Article  Google Scholar 

  15. Yao Y, Yang G, Sun X, Li L (2016) Detecting video frame-rate up-conversion based on periodic properties of edge-intensity. J Inf Secur Appl 26:39–50

    Google Scholar 

  16. Li Q, Wang R, Xu D (2018) An inter-frame forgery detection algorithm for surveillance video. Information (Switzerland) 9(12):301

    Google Scholar 

  17. Zhang Z, Hou J, Ma Q, Li Z (2015) Efficient video frame insertion and deletion detection based on inconsistency of correlations between local binary pattern coded frames. Secur Commun Netw 8(2):311–20

    Article  Google Scholar 

  18. Guo S, Wang J, Li Z, Zhang Z (2016) Video inter-frame forgery identification based on the consistency of quotient of MSSIM. Secur Commun Netw 5(October):4548–4556

    Google Scholar 

  19. ** X, Su Y, **g P (2022) Video frame deletion detection based on time – frequency analysis. J Vis Commun Image Represent 83:103436

  20. Singh G, Singh K (2019) Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation. Multimed Tools Appl 78(9):11527–11562

    Article  Google Scholar 

  21. Kingra S, Aggarwal N, Singh RD (2017) Inter-frame forgery detection in H.264 videos using motion and brightness gradients. Multimed Tools Appl 76(24):25767–25786

    Article  Google Scholar 

  22. Bakas J, Naskar R, Bakshi S (2021) Detection and localization of inter-frame forgeries in videos based on macroblock variation and motion vector analysis. Comput Electr Eng 89(November 2020):106929

    Article  Google Scholar 

  23. Zhao DN, Wang RK, Lu ZM (2018) Inter-frame passive-blind forgery detection for video shot based on similarity analysis. Multimed Tools Appl 77(19):25389–25408

    Article  Google Scholar 

  24. Fadl SM, Han Q, Li Q (2019) Inter-frame forgery detection based on differential energy of residue. IET Image Proc 13(3):522–528

    Article  Google Scholar 

  25. Arvind N, Singara S, Kasana S (2022) Multiple forgeries identification in digital video based on correlation consistency between entropy coded frames. Multimedia Syst 28(1):267–280

    Article  Google Scholar 

  26. Wang Y (2021) ENF based video forgery detection algorithm. Int J Digit Crime Forensics 12(1):131–156

    Article  CAS  Google Scholar 

  27. Li S, Huo H (2021) Frame deletion detection based on optical flow orientation variation. IEEE Access 9:37196–37209

    Article  Google Scholar 

  28. Liu J, Yan Z, Gan F, **ang J (2021) A fast forgery frame detection method for video copy - move inter / intra - frame identification. J Ambient Intell Humanized Comput 14:1647–1658

    Google Scholar 

  29. Ojala T, Pietikainen M, Maenpaa T (2000) Gray scale and rotation invariant texture classification with local binary patterns. Computer Vision-ECCV-2000(LNCS,1842): 404–420

  30. Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vision 7(1):11–32

    Article  Google Scholar 

  31. Bhattacharyya A (1946) On a measure of divergence between two multinomial populations. Sankhya Indian J Stat 401–406

  32. Qadir G, Yahaya S, Ho ATS (2012) Surrey University Library for Forensic Analysis (SULFA) of video content. In: IET Conference on Image Processing Publications (600 CP).

  33. Shelke NA, Kasana SS (2022) Multiple forgery detection and localization technique for digital video using PCT and NBAP. Multimed Tools Appl 81(16):22731–22759

    Article  Google Scholar 

  34. Bakas J, Naskar R, Dixit R (2019) Detection and localization of inter-frame video forgeries based on inconsistency in correlation distribution between Haralick coded frames. Multimed Tools Appl 78(4):4905–4935

    Article  Google Scholar 

  35. AbbasiAghamaleki J, Behrad A (2016) Inter-frame video forgery detection and localization using intrinsic effects of double compression on quantization errors of video coding. Signal Process Image Commun 47:289–302

    Article  Google Scholar 

Download references

Funding

This work is funded by University Grants Commission, Govt. of India to carry out the research.

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  1. Department of Information Technology, University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Shehnaz & Mandeep Kaur

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  1. Shehnaz
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  2. Mandeep Kaur
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Correspondence to Shehnaz.

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Shehnaz, Kaur, M. Detection and localization of multiple inter-frame forgeries in digital videos. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18263-3

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