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A passive blind forgery detection technique to identify frame duplication attack

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Abstract

This paper presents passive blind forgery detection to identify frame duplication attack in Moving Picture Experts Group-4 (MPEG-4) video. In this attack, one or more frames are copied and pasted at other location in the same video to hide or highlight particular activity. Since the tampered frames are from the same video, their statistical properties are uniform, which makes challenging to identify duplicate frames. In this paper, a two-step algorithm is proposed, in which the suspicious frames are identified, their features are extracted and compared with other frames of the test video to take the decision. Scale Invariant Feature Transform (SIFT) key-points are used as feature for comparison. Finally, Random Sample Consensus algorithm is used to locate duplicate frames. The proposed method is tested on compressed and uncompressed videos with variable compression rate. The simulation results show that the proposed scheme is competent to detect the tampered frames with 99.8% average accuracy. Comparative analysis is made for the proposed method with existing methods with respect to parameters Precision Rate (PR), Recall Rate (RR), Detection Accuracy (DA). The average values of PR, RR, and DA for the proposed method are 99.9%, 99.7%, 99.8% respectively, which are better than other methods. The proposed method needs average 33 seconds of simulation time, which is less as compared to other methods.

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References

  1. Al-Sanjary IsO, Sulong G (2015) Detection of video forgery: a review of literature. J Theor Appl Inf Technol, 74(2)

  2. Al-Sanjary OI, Ghazali N, Ahmed AA, Sulong G (2018) Semi-automatic methods in video forgery detection based on multi-view dimension, recent trends in information and communication technology. Lecture Notes on Data Engineering and Communications Technologies, 5. https://doi.org/10.1007/978-3-319-59427-9-41

  3. Aparicio-Daz E, Cumplido R, Perez Gort ML, Feregrino-Uribe C (2019) Temporal copy-move forgery detection and localization using block correlation matrix. J Intell Fuzzy Syst 36:5023–5035. https://doi.org/10.3233/JIFS-179048. IOS Press

    Article  Google Scholar 

  4. Bozkurt I, Bozkurt M, UlutaŞ G (2017) A new video forgery detection approach based on forgery line. Turkish J Electr Eng Comput Sci 25(6):4558–4574

    Article  Google Scholar 

  5. Fadl SM, Han Q, Li Q (2017) Authentication of surveillance videos: detecting frame duplication based on residual frame. J Forensic Science, 1–11, https://doi.org/10.1111/1556-4029.13658

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

    Article  Google Scholar 

  7. Gupta A, Gupta A, Mehra A (2015) Video authentication in digital forensic. In: International conference on futuristic trends on computational analysis and knowledge management, pp 659–663

  8. Hsu C, Hung T, Lin C, Hsu C (2008) Video forgery detection using correlation of noise residue. In: 2008 IEEE 10th workshop on multimedia signal processing, pp 170–174

  9. Hu Y, Li C, Wang Y, Liu B (2012) An improved fingerprinting algorithm for detection of video frame duplication forgery. Int J Digit Crime Forens (IJDCF) 4 (3):20–32

    Article  Google Scholar 

  10. Kingra S, Aggarwal N, Singh RD (2017) Video inter-frame forgery detection approach for surveillance and mobile recorded videos. Int J Electr Comput Eng (IJECE) 7(2):831,841

    Google Scholar 

  11. Li R, Zeng B, Liou ML (1994) A new three-step search algorithm for block motion estimation. IEEE Trans Circ Syst Vid Technol 4:4

    Google Scholar 

  12. Li Z, Zhang Z, Guo S, Wang J (2016) Video inter-frame forgery identification based on the consistency of quotient of MSSIM. Secur Commun Netw 9:4548–4556. https://doi.org/10.1002/sec.1648. Wiley

    Article  Google Scholar 

  13. Lin G, Chang J (2012) Detection of frame duplication forgery in videos based on spatial and temporal analysis. Proceedings of the 9th Workshop on Multimedia & Security 26(7):1250017

    MathSciNet  Google Scholar 

  14. Lin C, Tsay J (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 

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

    Article  Google Scholar 

  16. Pandey R, Singh S, Shukla KK (2014) Passive copy-move forgery detection in videos. In: 2014 International conference on computer and communication technology (ICCCT), pp 301–306

  17. Sharma S, Dhavale SV (2016) A review of passive forensic techniques for detection of copy-move attacks on digital videos. In: 2016 3rd International conference on advanced computing and communication systems (ICACCS), vol 1. IEEE, pp 1–6

  18. Singh RD, Aggarwal N (2017) Optical flow and prediction residual based hybrid forensic system for inter-frame tampering detection. J Circ Syst Comput 26(7):1750107. (37 pages)

    Article  Google Scholar 

  19. Singh RD, Aggarwal N (2018) Video content authentication techniques: a comprehensive survey. Multimed Syst 24:211–240. https://doi.org/10.1007/s00530-017-0538-9

    Article  Google Scholar 

  20. Singh G, Singh K (2018) Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-018-6585-1

  21. Singh V, Pant P, Tripathi R (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, pp 29–38

  22. Subramanyam AV, Emmanuel S (2012) Video forgery detection using HOG features and compression properties. MMSP, 89–94

  23. Ulutas G, Ustubioglu B, Ulutas M, Nabiyev V (2017) Frame duplication detection based on BoW model. Multimed Syst, 1–19

  24. 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 

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

  26. Yang J, Huang T, Su L (2016) Using similarity analysis to detect frame duplication forgery in videos. Multimed Tools Appl 75(4):1793–1811

    Article  Google Scholar 

  27. 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 

  28. Zhao D-N, Wang R-K, Lu Z-M (2018) Inter-frame passive-blind forgery detection for video shot based on similarity analysis. Multimedia Tools Applications, 9. https://doi.org/10.1007/s11042-018-5791-1

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Correspondence to Jayashree Kharat.

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Kharat, J., Chougule, S. A passive blind forgery detection technique to identify frame duplication attack. Multimed Tools Appl 79, 8107–8123 (2020). https://doi.org/10.1007/s11042-019-08272-y

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