Skip to main content
Log in

Multiple forgery detection and localization technique for digital video using PCT and NBAP

  • 1169: Interdisciplinary Forensics: Government, Academia and Industry Interaction
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

With the use of easily available video editing gadgets and applications, the authenticity of digital video has now become a significant concern since the last decade. Digital video forgeries based on inter and intra-frames are often used these days for generating false/unlawful means of evidence in many civil/criminal trials. In this paper, a passive technique using the Polar Cosine Transform (PCT) and Neighborhood Binary Angular Pattern (NBAP) along with the GoogleNet model is proposed to detect and localize multiple forgeries in the video. The pre-processing algorithm is used to extract the frames from the input videos and transform 3-D RGB color space to 2-D grayscale space. The features are then extracted using the PCT and NBAP methodologies. The extracted features are then fed to the pre-trained GoogleNet model to identify the forgeries in the video. The proposed technique can detect and localize both intra-frame forgeries and inter-frame forgeries present in the video. Besides, the proposed technique is robust enough to handle the multiple forgeries in the video, even if the video is subjected to noise exposures. With the help of performance measures like accuracy, specificity, precision, recall, and F1-score, we conduct a comprehensive performance assessment to validate the effectiveness of the proposed technique.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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
Fig. 24
Fig. 25

Similar content being viewed by others

Notes

  1. ffmpeg software available online at https://www.ffmpeg.org/

References

  1. Aghamaleki JA, Behrad A (2017) Malicious inter-frame video tampering detection in mpeg videos using time and spatial domain analysis of quantization effects. Multimed Tools Appl 76(20):20691–20717

    Article  Google Scholar 

  2. Aloraini M, Sharifzadeh M, Schonfeld D (2020) Sequential and patch analyses for object removal video forgery detection and localization. IEEE Transactions on Circuits and Systems for Video Technology

  3. 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 

  4. Benrhouma O, Hermassi H, Abd El-Latif AA, Belghith S (2016) Chaotic watermark for blind forgery detection in images. Multimed Tools Appl 75 (14):8695–8718

    Article  Google Scholar 

  5. 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, Springer, pp 267–281

  6. Chen S, Tan S, Li B, Huang J (2016) Automatic detection of object-based forgery in advanced video. IEEE Trans Circuits Syst Video Technol 26 (11):2138–2151

    Article  Google Scholar 

  7. D’Amiano L, Cozzolino D, Poggi G, Verdoliva L (2018) A patchmatch-based dense-field algorithm for video copy–move detection and localization. IEEE Trans Circuits Syst Video Technol 29(3):669–682

    Article  Google Scholar 

  8. D’Avino D, Cozzolino D, Poggi G, Verdoliva L (2017) Autoencoder with recurrent neural networks for video forgery detection. Electron Imaging 2017(7) 92–99

  9. 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 

  10. GRIP (2018) Copy-move Dataset: [Online] http://www.grip.unina.it/download/prog/ForgedVideosDataset/Copymove/ last accessed on 24 Nov 2019

  11. Kharat J, Chougule S (2020) A passive blind forgery detection technique to identify frame duplication attack. Multimed Tools Appl 1–17

  12. 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 

  13. KTH (Accessed 24 Nov 2019) Database:[Online] http://www.nada.kth.se/cvap/actions

  14. Liu Y, Huang T (2017) Exposing video inter-frame forgery by zernike opponent chromaticity moments and coarseness analysis. Multimed Syst 23(2):223–238

    Article  Google Scholar 

  15. Long C, Smith E, Basharat A, Hoogs A (2017) A c3d-based convolutional neural network for frame dropping detection in a single video shot. In: 2017 IEEE conference on computer vision and pattern recognition workshops. CVPRW, IEEE, 1898–1906

  16. Oh S, Hoogs A, Perera A, Cuntoor N, Chen CC, Lee JT, Mukherjee S, Aggarwal J, Lee H, Davis L, et al. (2011) A large-scale benchmark dataset for event recognition in surveillance video. In: CVPR 2011, IEEE, pp 3153–3160

  17. Pandey RC, Singh SK, Shukla K (2014) Passive copy-move forgery detection in videos. In: 2014 International conference on computer and communication technology. ICCCT, IEEE, 301–306

  18. Qadir G, Yahaya S, Ho ATS (2012) Surrey university library for forensic analysis (sulfa) of video content pp 1–6. http://sulfa.cs.surrey.ac.uk/

  19. REWIND (2013) Datset: [Online]. https://sites.google.com/site/rewindpolimi/downloads/datasets/video-copy-move-forgeries-datase Accessed 2 Nov 2019

  20. Sedik A, Iliyasu AM, El-Rahiem A, Abdel Samea ME, Abdel-Raheem A, Hammad M, Peng J, El-Samie A, Fathi E, El-Latif AAA, et al. (2020) Deploying machine and deep learning models for efficient data-augmented detection of covid-19 infections. Viruses 12(7):769

    Article  Google Scholar 

  21. Shelke NA, Kasana SS (2020) A comprehensive survey on passive techniques for digital video forgery detection. Multimed Tools Appl 1–64

  22. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:14091556

  23. Singh RD, Aggarwal N (2017a) Detection and localization of copy-paste forgeries in digital videos. Forensic Sci Inter 281:75–91

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. 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 Multimed 20(4):825–840

    Article  Google Scholar 

  26. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  27. TREC (Accessed 2 Nov 2019) Video Retrieval Evaluation:: TRECVID Dataset [Online] http://trecvid.nist.gov/

  28. VTL (Accessed 24 Nov 2019) Video Trace Library: [Online] http://trace.eas.asu.edu/

  29. Wei W, Fan X, Song H, Wang H (2019) Video tamper detection based on multi-scale mutual information. Multimed Tools Appl 78(19):27109–27126

    Article  Google Scholar 

  30. Yao Y, Shi Y, Weng S, Guan B (2017) Deep learning for detection of object-based forgery in advanced video. Symmetry 10(1):3

    Article  Google Scholar 

  31. Zhang G, Liang G, Li W, Fang J, Wang J, Geng Y, Wang JY (2017) Learning convolutional ranking-score function by query preference regularization. In: International conference on intelligent data engineering and automated learning, Springer, pp 1–8

  32. 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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nitin Arvind Shelke.

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

Shelke, N.A., Kasana, S.S. Multiple forgery detection and localization technique for digital video using PCT and NBAP. Multimed Tools Appl 81, 22731–22759 (2022). https://doi.org/10.1007/s11042-021-10989-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-10989-8

Keywords

Navigation