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An improved surveillance video forgery detection technique using sensor pattern noise and correlation of noise residues

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Abstract

Digital videos have become an important aspect of our lives lately, from a personal memorable to surveillance videos which can be presented in a court as an evidence now, this video evidence can be very important for the court of law and the investigators to understand the events as they occurred. Huge inventions in the video editing world have resulted in a margin of doubt, to the point where courts are now reluctant to accept any video evidence in to their case files, as for a digital video to be presented as an evidence, it’s integrity and authenticity is very important. This progression in technology can push the courts to a point where they refuse to trust anything that was caught on a video. Thus, more efforts have to be made in the video forensics world to avoid such a future. It has been shown that sources induce their specific noise patterns (i.e. Sensor pattern noise) to the captured media, and these patterns can be used to trace the source sensor and also to detect any forgeries. Acquiring SPN (sensor pattern noise) consists of two steps, first to get noise residues by subtracting denoised frames from the actual frames taken from the sample data and then to average those noise residues to get the sensor pattern noise. Problem arises when the attacker can also extract sensor pattern noise or has access to it. The attacker can induce SPN in forged frames which will prevent detection using the previous SPN based detection technique. This paper first proposes how the attacker can compensate a forged image by inducing SPN in it and then proposes a forgery detection technique for such a scenario which would not only rely on noise residue correlation with SPN but correlation with noise residue from the previous frame as well. We first estimate sensor pattern noise based on locally adaptive discrete cosine transform, then we correlate noise residue of frame n under investigation with the sensor pattern noise and with the noise residue of the previous frame n-1 as well. Now, even if the attacker compensates the forged frame by inducing SPN in it, the inter-frame continuity of noise shall be disturbed hence identified. Simulations using benchmark dataset for SPN extraction technique and custom dataset for video forgery detection, show that this methodology successfully detects any forgeries and locate their positions in the normal case and in the case where SPN is induced in the forged frames as well.

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References

  1. Altinisik E, Tasdemir K, Sencar HT (2018) Extracting Prnu Noise from H.264 Coded Videos. In: 2018 26th European signal processing conference (EUSIPCO). pp 1367–1371. IEEE.

  2. Ba Z, Qin Z, Fu X, Ren K (2019) CIM: camera in motion for smartphone authentication. IEEE Trans Inf Forensics Secur 14(11):2987–3002

    Article  Google Scholar 

  3. Bestagini P, Milani S, Tagliasacchi M, Tubaro S (2013) Local tampering detection invideo sequences. In: 2013 IEEE 15th international workshop on multimedia signal processing (MMSP), pp 488–493

  4. Cooper AJ (2013) Improved photo response non-uniformity (PRNU) based source camera identification. Forensic Sci Int 226(1–3), 132–141.

  5. Fadl SM, Han Q, Li Q (2018) Authentication of surveillance videos: detecting frame duplication based on residual frame. J Forensic Sci 63(4):1099–1109

    Article  Google Scholar 

  6. Hosler B et al (2019) A video camera model identification system using deep learning and fusion. In: ICASSP 2019–2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). Brighton, pp 8271–8275

  7. Hsu YF, Chang SF (2007) Image splicing detection using camera response function consistency and automatic segmentation. In: 2007 IEEE international conference on multimedia and expo, pp 28–31

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

  9. Huang T, Zhang X, Huang W, Lin L, Weifeng S (2018) A multi-channel approach through fusion of audio for detecting video inter-frame forgery. Comput Secur 77:412–426

    Article  Google Scholar 

  10. Huh JH (2017) PLC-based design of monitoring system for ICT-integrated vertical fish farm. Human-centric Comput Inf Sci 7(1):20

    Article  Google Scholar 

  11. Hyun D-K, Lee M-J, Ryu S-J, Lee H-Y, Lee H-K (2013) Forgery detection for surveillance video. Springer New York, New York, pp 25–36

    Google Scholar 

  12. Jeyalakshmi A, Chitra DR (2018) Comparative study of feature extraction using several wavelet transforms for source camera identification. In: 2018 2nd international conference on inventive systems and control (ICISC), Coimbatore, pp 1407–1414

  13. Jia S, Xu Z, Wang H, Feng C, Wang T (2018) Coarse-to-fine copy-move forgery detection for video forensics. IEEE Access 6:25323–25335

    Article  Google Scholar 

  14. 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 Process Lett 20(5):447–450

    Article  Google Scholar 

  15. Jung SH, Huh JH (2019) A novel on transmission line tower big data analysis model using altered K-means and ADQL. Sustainability 11(13):3499

    Article  Google Scholar 

  16. In: Security, Forensics, Steganography, and Watermarking of Multimedia Contents X (Vol. 6819, p. 68190G). International Society for Optics and Photonics.

  17. Lawgaly A, Khelifi F (2017) Sensor pattern noise estimation based on improvedlocallyadaptivedctfilteringandweightedaveragingforsource cameraidentificationandverification. IEEE Trans Inf Forensics Secur 12(2):392–404

    Article  Google Scholar 

  18. Le QV, Ngiam J, Coates A, Lahiri A, Prochnow B, Ng AY (2011) On optimization methods for deep learning. In: Proceedings of the 28th international conference on international conference on machine learning. Omnipress, pp 265–272

  19. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444.

  20. Lin X, Li CT (2016) Preprocessing reference sensor pattern noise via spectrum equalization. IEEE Trans Inf Forensics Secur 11(1):126–140

    Article  Google Scholar 

  21. Lin X, Li CT (2016) Enhancing sensor pattern noise via filtering distortion removal. IEEE Signal Process Lett 23(3):381–385

    Article  Google Scholar 

  22. Liu Y, Huang T, Liu Y (2018) A novel video forgery detection algorithm for blue screencompositing based on 3-stage foreground analysis and tracking. Multimed Tools Appl 77(6):7405–7427

    Article  Google Scholar 

  23. Lukas J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1(2):205–214

    Article  Google Scholar 

  24. Mahmood T, Mehmood Z, Shah M, Saba T (2018) A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform. J Vis Commun Image Represent 53:202–214

    Article  Google Scholar 

  25. Malik SUR, Khan SU, Srinivasan SK (2013) Modeling and analysis of state-of-the-art VM-based cloud management platforms. In: IEEE transactions on cloud computing, pp 50–63

  26. Mehrish A, Subramanyam AV, Emmanuel S (2016) Sensor pattern noise estimation using probabilistically estimated raw values. IEEE Signal Process Lett 23(5):693–697

    Article  Google Scholar 

  27. Pandey RC, Singh SK, Shukla KK (2014) Passive copy-move forgery detection in videos. In: 2014 international conference on computer and communication technology (ICCCT), pp 301–306

  28. Richao C, Yang G, Zhu N (2014) Detection of objectbased manipulation by the statistical features of object contour. Forensic Sci Int 236:164–169

    Article  Google Scholar 

  29. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  30. Seo YS, Huh JH (2019) Automatic emotion-based music classification for supporting intelligent IoT applications. Electronics 8(2):164

    Article  Google Scholar 

  31. Su L, Li C, Lai Y, Yang J (2018) A fast forgery detection algorithm based on exponential-fourier moments for video region duplication. IEEE Trans Multimedia 20(4):825–840

    Article  Google Scholar 

  32. Wahab AWA, Bagiwa MA, Idris MYI, Khan S, Razak Z, Ariffin MRK (2014) Passive video forgery detection techniques: a survey. In 2014 10th international conference on information assurance and security. IEEE, pp 29–34

  33. Wu G, Kang X, Liu KJR (2012) A context adaptive predictor of sensor pattern noise for camera source identification. In: 2012 19th IEEE international conference on image processing, Orlando, pp 237–240

  34. Xu J, Liang Y, Tian X, Xie A (2016) A novel video inter-frame forgery detection method based on histogram intersection. In: 2016 IEEE/CIC international conference on communications in China (ICCC), pp 1–6

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

    Article  Google Scholar 

  36. Yaqub W, Mohanty M, Memon N (2018) Towards camera identification from cropped query images. In: 2018 25th IEEE international conference on image processing (ICIP), Athens, pp 3798–3802. IEEE.

  37. Zhao D-N, Wang R-K, Lu Z-M (2018) Inter-frame passive-blind forgery detection for video shot based on similarity analysis. Multimed Tools Appl 77(19):25389–25408

    Article  Google Scholar 

  38. Zhu Y, Guo C, Luo G (2018) A detection method for facial expression reenacted forgery in videos

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan

    Muhammad Aizad Fayyaz, Adeel Anjum, Sheikh Ziauddin, Ahmed Khan & Aaliya Sarfaraz

Authors
  1. Muhammad Aizad Fayyaz
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  2. Adeel Anjum
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  3. Sheikh Ziauddin
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  4. Ahmed Khan
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  5. Aaliya Sarfaraz
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Correspondence to Ahmed Khan.

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Mentioned authors have no conflict of interest upon this article. This article does not contain any studies with human participants or animals performed by any of the authors.

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Fayyaz, M.A., Anjum, A., Ziauddin, S. et al. An improved surveillance video forgery detection technique using sensor pattern noise and correlation of noise residues. Multimed Tools Appl 79, 5767–5788 (2020). https://doi.org/10.1007/s11042-019-08236-2

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  • DOI: https://doi.org/10.1007/s11042-019-08236-2

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