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A comprehensive survey on passive techniques for digital video forgery detection

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Abstract

Digital videos are one of the most widespread forms of multimedia in day to day life. These are widely transferred over social networking websites such as Facebook, Instagram, WhatsApp, YouTube, etc. through the Internet. Availability of modern and easy to use editing tools have facilitated the modification of the contents of the digital videos. Therefore, it has become an essential concern for the legitimacy, trustworthiness, and authenticity of these digital videos. Digital video forgery detection aims to identify the manipulations in the video and to check its authenticity. These techniques can be divided into active and passive techniques. In this paper, a comprehensive survey on video forgery detection using passive techniques have been presented. The primary goal of this survey is to study and analyze the existing passive video forgery detection techniques. Firstly, the preliminary information required for understanding video forgery detection is presented. Later, a brief survey of existing passive video forgery detection techniques based on the features, forgery identified, datasets used, and performance parameters detail along with their limitations are reviewed. Then, anti-forensics strategy and deepfake detection in the video are discussed. After that, standard benchmark video forgery datasets and the generalized architecture for passive video forgery detection techniques are discussed. Finally, few open challenges in the field of passive video forgery detection are also described.

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Notes

  1. The abbreviations for the feature discussed earlier mentioned in http://dde.binghamton.edu/download/feature_extractors/.

  2. The details of test video sequences are available at on Internet via URL: https://sites.google.com/site/multimediaforensic, (STCA, 2013).

  3. The Laplacian pyramid is a flexible data structure with several appealing features for image/video analysis.

  4. Inception V3 [118] is a CNN that is trained on over a million of images from ImageNet dataset.

References

  1. Afchar D, Nozick V, Yamagishi J, Echizen I (2018) Mesonet: a compact facial video forgery detection network. In: 2018 IEEE international workshop on information forensics and security (WIFS). IEEE, pp 1–7

  2. 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 Process Image Commun 47:289–302

    Google Scholar 

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

    Google Scholar 

  4. Al-Sanjary OI, Ahmed AA, Sulong G (2016) Development of a video tampering dataset for forensic investigation. Forensic Sci Int 266:565–572

    Google Scholar 

  5. Aloraini M, Sharifzadeh M, Agarwal C, Schonfeld D (2019) Statistical sequential analysis for object-based video forgery detection. Elect Imag 2019(5):543–1

    Google Scholar 

  6. Aloraini M, Sharifzadeh M, Schonfeld D (2020) Sequential and patch analyses for object removal video forgery detection and localization. IEEE Trans Circ Syst Vid Technol

  7. Amerini I, Galteri L, Caldelli R, Del Bimbo A (2019) Deepfake video detection through optical flow based cnn. In: Proceedings of the IEEE international conference on computer vision workshops, pp 0–0

  8. Aparicio-Díaz E, Cumplido R, Gort P, Lázaro M, Feregrino-Uribe C (2019) Temporal copy-move forgery detection and localization using block correlation matrix. J Intel Fuzz Sys 36(5):5023–5035

    Google Scholar 

  9. Ardizzone E, Mazzola G (2015) A tool to support the creation of datasets of tampered videos. In: International conference on image analysis and processing. Springer, pp 665–675

  10. Bagiwa MA, Wahab AWA, Idris MYI, Khan S, Choo KKR (2016) Chroma key background detection for digital video using statistical correlation of blurring artifact. Digit Invest 19:29–43

    Google Scholar 

  11. Bai S, Yao H, Ni R, Zhao Y (2019) Detection and localization of video object removal by spatio-temporal lbp coherence analysis. In: International conference on image and graphics. Springer, pp 244–254

  12. 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. Multimedia Tool Appl 78(4):4905–4935

    Google Scholar 

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

  14. Bidokhti A, Ghaemmaghami S (2015) Detection of regional copy/move forgery in mpeg videos using optical flow. In: 2015 The international symposium on artificial intelligence and signal processing (AISP). IEEE, pp 13–17

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

    Google Scholar 

  16. CANTATA (Accessed 2 Nov 2019) Dataset [Online]: http://www.multitel.be/cantata/

  17. 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

  18. Chen CC, Chen LY, Lin YJ (2017) Block sampled matching with region growing for detecting copy-move forgery duplicated regions. J Inf Hiding Multimed Signal Process 8(1):86–96

    Google Scholar 

  19. Chen R, Dong Q, Ren H, Fu J (2012) Video forgery detection based on non-subsampled contourlet transform and gradient information. Inf Technol J 11(10):1456–1462

    Google Scholar 

  20. Chen S, Tan S, Li B, Huang J (2016) Automatic detection of object-based forgery in advanced video. IEEE Trans Circ Sys Vid Tech 26(11):2138–2151

    Google Scholar 

  21. Chetty G, Biswas M, Singh R (2010) Digital video tamper detection based on multimodal fusion of residue features. In: 2010 Fourth international conference on network and system security. IEEE, pp 606–613

  22. Chittapur GB, Murali S, Prabhakara H, Anami BS (2014) Exposing digital forgery in video by mean frame comparison techniques. In: Emerging research in electronics, computer science and technology. Springer, pp 557–562

  23. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv:14061078

  24. D’Amiano L, Cozzolino D, Poggi G, Verdoliva L (2018) A patch match-based dense-field algorithm for video copy-move detection and localization. IEEE Trans Circ Sys Vid Technol 29:669–682

    Google Scholar 

  25. Dataset (Accessed 2 Dec 2019) [Online] https://spectrum.ieee.org/tech-talk/artificial-intelligence/machine-learning/facebook-ai-launches-its-deepfake-detection-challenge

  26. Dataset (Accessed 2 Nov 2019) Test Database: [Online] https://ceng2.ktu.edu.tr/gulutas/test_database.rar

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

    Google Scholar 

  28. Dong Q, Yang G, Zhu N (2012) A mcea based passive forensics scheme for detecting frame-based video tampering. Digit Invest 9(2):151–159

    Google Scholar 

  29. 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

    Google Scholar 

  30. Fan Y, Zhu YS, Liu Z (2016) An improved sift-based copy-move forgery detection method using t-linkage and multi-scale analysis. J Inf Hiding Multimedia Sign Process 7(2):399–408

    Google Scholar 

  31. Fayyaz MA, Anjum A, Ziauddin S, Khan A, Sarfaraz A (2020) An improved surveillance video forgery detection technique using sensor pattern noise and correlation of noise residues. Multimedia Tools Appl 79(9):5767–5788

    Google Scholar 

  32. 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, pp 171–179

  33. Gironi A, Fontani M, Bianchi T, Piva A, Barni M (2014) A video forensic technique for detecting frame deletion and insertion. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 6226–6230

  34. GRIP (2017) Splicing Dataset: [Online] http://www.grip.unina.it/download/prog/ForgedVideosDataset/Splicing/. Accessed 24 Nov 2019

  35. GRIP (2018) Copy-move Dataset: [Online] https://www.grip.unina.it/download/prog/ForgedVideosDataset/Copymove/. Accessed 24 Nov 2019

  36. Guan H, Kozak M, Robertson E, Lee Y, Yates AN, Delgado A, Zhou D, Kheyrkhah T, Smith J, Fiscus J (2019) Mfc datasets: Large-scale benchmark datasets for media forensic challenge evaluation. In: 2019 IEEE winter applications of computer vision workshops (WACVW). IEEE, pp 63–72

  37. Güera D, Delp EJ (2018) Deepfake video detection using recurrent neural networks. In: 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE, pp 1–6

  38. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  39. Hong JH, Yang Y, Oh BT (2019) Detection of frame deletion in hevc-coded video in the compressed domain. Digit Invest 30:23–31

    Google Scholar 

  40. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:170404861

  41. Hsu CC, Hung TY, Lin CW, Hsu CT (2008) Video forgery detection using correlation of noise residue. In: 2008 IEEE 10th workshop on multimedia signal processing. IEEE, pp 170–174

  42. Hu X, Ni J, Pan R (2015) Detecting video forgery by estimating extrinsic camera parameters. In: International workshop on digital watermarking. Springer, pp 28–38

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

    Google Scholar 

  44. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  45. Hyun DK, Lee MJ, Ryu SJ, Lee HY, Lee HK (2013) Forgery detection for surveillance video. In: The era of interactive media. Springer, pp 25–36

  46. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia, pp 675–678

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

    Google Scholar 

  48. Johnston P, Elyan E, Jayne C (2019) Video tampering localisation using features learned from authentic content. Neural Comput Appl 32:12243–12257

    Google Scholar 

  49. Kancherla K, Mukkamala S (2012) Novel blind video forgery detection using markov models on motion residue. In: Asian conference on intelligent information and database systems. Springer, pp 308–315

  50. Kang X, Liu J, Liu H, Wang ZJ (2016) Forensics and counter anti-forensics of video inter-frame forgery. Multimedia Tools Appl 75(21):13833–13853

    Google Scholar 

  51. Kharat J, Chougule S (2020) A passive blind forgery detection technique to identify frame duplication attack. Multimedia Tools Appl 79:8107–8123

    Google Scholar 

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

    Google Scholar 

  53. Kobayashi M, Okabe T, Sato Y (2009) Detecting video forgeries based on noise characteristics. In: Pacific-rim symposium on image and video technology. Springer, pp 306–317

  54. Kobayashi M, Okabe T, Sato Y (2010) Detecting forgery from static-scene video based on inconsistency in noise level functions. IEEE Trans Inf Forensics Sec 5(4):883–892

    Google Scholar 

  55. Kono K, Yoshida T, Ohshiro S, Babaguchi N (2018) Passive video forgery detection considering spatio-temporal consistency. In: International conference on soft computing and pattern recognition. Springer, pp 381–391

  56. Koopman M, Rodriguez AM, Geradts Z (2018) Detection of deepfake video manipulation. In: The 20th Irish machine vision and image processing conference (IMVIP), pp 133–136

  57. Korshunov P, Marcel S (2018) Deepfakes: a new threat to face recognition? Assessment and detection. arXiv:181208685

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

  59. Labartino D, Bianchi T, De Rosa A, Fontani M, Vázquez-Padín D, Piva A, Barni M (2013) Localization of forgeries in mpeg-2 video through gop size and dq analysis. In: 2013 IEEE 15th international workshop on multimedia signal processing (MMSP). IEEE, pp 494–499

  60. Li F, Huang T (2014) Video copy-move forgery detection and localization based on structural similarity. In: Proceedings of the 3rd international conference on multimedia technology (ICMT 2013). Springer, pp 63–76

  61. Li L, Wang X, Zhang W, Yang G, Hu G (2012) Detecting removed object from video with stationary background. In: International workshop on digital watermarking. Springer, pp 242–252

  62. Li L, Li S, Zhu H, Chu SC, Roddick JF, Pan JS (2013) An efficient scheme for detecting copy-move forged images by local binary patterns. J Inf Hiding Multimedia Sig Process 4(1):46–56

    Google Scholar 

  63. Li Y, Lyu S (2019) Exposing deepfake videos by detecting face warping artifacts. In: The IEEE conference on computer vision and pattern recognition (CVPR) workshops

  64. Li Y, Chang MC, Lyu S (2018) In ictu oculi: exposing ai created fake videos by detecting eye blinking. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, pp 1–7

  65. Li Z, Zhang Z, Guo S, Wang J (2016) Video inter-frame forgery identification based on the consistency of quotient of mssim. Sec and Commun Netw 9(17):4548–4556. https://doi.org/10.1002/sec.1648

    Article  Google Scholar 

  66. Liao SY, Huang TQ (2013) Video copy-move forgery detection and localization based on tamura texture features. 2013 6th International Congress on Image and Signal Processing (CISP) 2:864–868

    Google Scholar 

  67. Lin CS, Tsay JJ (2013) Passive approach for video forgery detection and localization. In: The second international conference on cyber security, cyber peacefare and digital forensic (CyberSec2013), pp 107–112

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

    Google Scholar 

  69. Lin GS, Chang JF (2012) Detection of frame duplication forgery in videos based on spatial and temporal analysis. Int J Pat Recogn Artif Intell 26 (07):1250017

    MathSciNet  Google Scholar 

  70. Liu H, Li S, Bian S (2014) Detecting frame deletion in h. 264 video. In: International conference on information security practice and experience. Springer, pp 262–270

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

    MathSciNet  Google Scholar 

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

    Google Scholar 

  73. 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, pp 1898–1906

  74. Mathai M, Rajan D, Emmanuel S (2016) Video forgery detection and localization using normalized cross-correlation of moment features. In: 2016 IEEE Southwest symposium on image analysis and interpretation (SSIAI). IEEE, pp 149–152

  75. Mizher MA, Ang MC, Mazhar AA, Mizher MA (2017) A review of video falsifying techniques and video forgery detection techniques. Int J Elec Sec Digit Forensics 9(3):191–208

    Google Scholar 

  76. Mondaini N, Caldelli R, Piva A, Barni M, Cappellini V (2007) Detection of malevolent changes in digital video for forensic applications. In: Security, steganography, and watermarking of multimedia contents IX, International Society for Optics and Photonics, vol 6505, p 65050T

  77. Nguyen HH, Yamagishi J, Echizen I (2019) Capsule-forensics: using capsule networks to detect forged images and videos. In: ICASSP 2019-2019 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2307–2311

  78. Nguyen TT, Nguyen CM, Nguyen DT, Nguyen DT, Nahavandi S (2019) Deep learning for deepfakes creation and detection. arXiv:190911573

  79. NIMBLE (Accessed 24 Nov 2019) Nimble Challenge 2017 Dataset: [Online] https://www.nist.gov/itl/iad/mig/nimble-challenge-2017-evaluation

  80. NTHU (Accessed 22 Nov 2019a) Forensics Project Dataset [Online]: http://www.ee.nthu.edu.tw/cwlin/forensics/forensics.htm

  81. NTHU (Accessed 22 Nov 2019b) Video Inpainting Project: http://www.ee.nthu.edu.tw/cwlin/inpainting/inpainting.htm

  82. 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

  83. 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, pp 301–306

  84. Pandey RC, Singh SK, Shukla KK (2016) Passive forensics in image and video using noise features: a review. Digit Invest 19:1–28

    Google Scholar 

  85. Pu H, Huang T, Guo G, Weng B, You L (2019) Video tampering detection algorithm based on spatial constraints and stable feature. In: UK workshop on computational intelligence. Springer, pp 541–553

  86. 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/

  87. Ravi H, Subramanyam AV, Gupta G, Kumar BA (2014) Compression noise based video forgery detection. In: 2014 IEEE international conference on image processing (ICIP). IEEE, pp 5352–5356

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

  89. Rocha A, Scheirer W, Boult T, Goldenstein S (2011) Vision of the unseen: current trends and challenges in digital image and video forensics. ACM Comput Surv (CSUR) 43(4):1–42

    Google Scholar 

  90. Rössler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M (2018) Faceforensics: a large-scale video dataset for forgery detection in human faces. arXiv:180309179

  91. Rossler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M (2019) Faceforensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE international conference on computer vision, pp 1–11

  92. Sabir E, Cheng J, Jaiswal A, AbdAlmageed W, Masi I, Natarajan P (2019) Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI) 3:1

    Google Scholar 

  93. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in neural information processing systems, pp 3856–3866

  94. Saddique M, Asghar K, Bajwa UI, Hussain M, Habib Z (2019) Spatial video forgery detection and localization using texture analysis of consecutive frames. Adv Elect Comput Eng 19(3):97–108

    Google Scholar 

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

    Google Scholar 

  96. Shelke NA, Chatur P (2013) A survey on various digital video watermarking schemes. IJCSET 4(12)

  97. Shelke NA, Chatur P (2016) Optimized and hybrid based watermarking system for digital video security. In: 2016 International conference on wireless communications, signal processing and networking (WiSPNET). IEEE, pp 1068–1074

  98. Shih TK, Tang NC, Tsai JC, Hwang JN (2010) Video motion interpolation for special effect applications. IEEE Trans Syst Man Cybernetics Part C (Applications and Reviews) 41(5):720–732

    Google Scholar 

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

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  103. Singh RD, Aggarwal N (2017c) Optical flow and prediction residual based hybrid forensic system for inter-frame tampering detection. J Circ Syst Comput 26(07):1750107

    Google Scholar 

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

    Google Scholar 

  105. 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. Springer, pp 29–38

  106. Sitara K, Mehtre BM (2016) Digital video tampering detection: an overview of passive techniques. Digit Invest 18:8–22

    Google Scholar 

  107. Sitara K, Mehtre BM (2017) A comprehensive approach for exposing inter-frame video forgeries. In: 2017 IEEE 13th international colloquium on signal processing its applications (CSPA), pp 73–78. https://doi.org/10.1109/CSPA.2017.8064927

  108. Stamm MC, Lin WS, Liu KJR (2012) Temporal forensics and anti-forensics for motion compensated video. IEEE Trans Inf Forensics Sec 7(4):1315–1329

    Google Scholar 

  109. Su L, Li C (2018) A novel passive forgery detection algorithm for video region duplication. Multidim Syst Sign Process 29(3):1173–1190

    MathSciNet  MATH  Google Scholar 

  110. Su L, Huang T, Yang J (2015a) A video forgery detection algorithm based on compressive sensing. Multimedia Tools Appl 74(17):6641–6656

    Google Scholar 

  111. 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

    Google Scholar 

  112. Su L, Luo H, Wang S (2019) A novel forgery detection algorithm for video foreground removal. IEEE Access 7:109719–109728

    Google Scholar 

  113. Su PC, Suei PL, Chang MK, Lain J (2015b) Forensic and anti-forensic techniques for video shot editing in h. 264/avc. J Vis Commun Image Represent 29:103–113

    Google Scholar 

  114. Su Y, Zhang J, Liu J (2009) Exposing digital video forgery by detecting motion-compensated edge artifact. In: 2009 international conference on computational intelligence and software engineering. IEEE, pp 1–4

  115. Subramanyam AV, Emmanuel S (2012) Video forgery detection using hog features and compression properties. In: 2012 IEEE 14th international workshop on multimedia signal processing (MMSP). IEEE, pp 89–94

  116. Subramanyam AV, Emmanuel S (2013) Pixel estimation based video forgery detection. In: 2013 IEEE International conference on acoustics, speech and signal processing. IEEE, pp 3038–3042

  117. SULFA (Accessed 1 Nov 2019) Surrey University Library for Forensic Analysis Dataset [Online]: http://sulfa.cs.surrey.ac.uk/

  118. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  119. Tan S, Chen S, Li B (2015) Gop based automatic detection of object-based forgery in advanced video. In: 2015 Asia-pacific signal and information processing association annual summit and conference (APSIPA). IEEE, pp 719–722

  120. Tralic D, Grgic S, Zovko-Cihlar B (2014) Video frame copy-move forgery detection based on cellular automata and local binary patterns. In: 2014 X international symposium on telecommunications (BIHTEL). IEEE, pp 1–4

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

  122. Ulutas G, Ustubioglu B, Ulutas M, Nabiyev V (2017) Frame duplication/mirroring detection method with binary features. IET Image Process 11(5):333–342

    Google Scholar 

  123. Ulutas G, Ustubioglu B, Ulutas M, Nabiyev VV (2018) Frame duplication detection based on bow model. Multimedia Sys 24(5):549–567

    Google Scholar 

  124. VIUCM (Accessed 24 Nov 2019) Video Inpainting Under Camera Motion: [Online] http://www.tc.umn.edu/patw0007/video-inpainting/

  125. VTD (Accessed 2 Nov 2019) Video Tampering Dataset: [Online]: https://www.youtube.com/channel/UCZuuu-iyZvPptbIUHT9tMrA

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

  127. 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

  128. Wang Q, Li Z, Zhang Z, Ma Q (2014a) Video inter-frame forgery identification based on consistency of correlation coefficients of gray values. J Comput Commun 2(04):51

    Google Scholar 

  129. Wang Q, Li Z, Zhang Z, Ma Q (2014b) Video inter-frame forgery identification based on optical flow consistency. Sensors & Transducers 166 (3):229

    Google Scholar 

  130. 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, pp 37–47

  131. Wang W, Farid H (2007) Exposing digital forgeries in interlaced and deinterlaced video. IEEE Trans Inf Forensics Sec 2(3):438–449

    Google Scholar 

  132. 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

  133. 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, pp 39–48

  134. Wang W, Jiang X, Wang S, Wan M, Sun T (2013) Identifying video forgery process using optical flow. In: International workshop on digital watermarking. Springer, pp 244–257

  135. Wang Y, Jodoin PM, Porikli F, Konrad J, Benezeth Y, Ishwar P (2014) Cdnet 2014: An expanded change detection benchmark dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 387–394

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

    Google Scholar 

  137. Wu Y, Jiang X, Sun T, Wang W (2014) Exposing video inter-frame forgery based on velocity field consistency. In: 2014 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2674–2678

  138. Xiph (Accessed 24 Nov 2019) Dataset [Online]: www.xiph.org

  139. 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). IEEE, pp 1–6

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

    Google Scholar 

  141. Yang X, Li Y, Lyu S (2019) Exposing deep fakes using inconsistent head poses. In: ICASSP 2019-2019 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 8261–8265

  142. Yao H, Ni R, Zhao Y (2019) An approach to detect video frame deletion under anti-forensics. J Real-Time Image Proc 16(3):751–764

    Google Scholar 

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

    Google Scholar 

  144. YFCC (Accessed 24 Nov 2019) 3YFCC100m Dataset: [Online] http://www.yfcc100m.org

  145. Yin L, Bai Z, Yang R (2014) Video forgery detection based on nonnegative tensor factorization. In: 2014 4th IEEE international conference on information science and technology. IEEE, pp 148–151

  146. Yu L, Wang H, Han Q, Niu X, Yiu SM, Fang J, Wang Z (2016) Exposing frame deletion by detecting abrupt changes in video streams. Neurocomputing 205:84–91

    Google Scholar 

  147. Zampoglou M, Markatopoulou F, Mercier G, Touska D, Apostolidis E, Papadopoulos S, Cozien R, Patras I, Mezaris V, Kompatsiaris I (2019) Detecting tampered videos with multimedia forensics and deep learning. In: International conference on multimedia modeling. Springer, pp 374–386

  148. Zhang J, Su Y, Zhang M (2009) Exposing digital video forgery by ghost shadow artifact. In: Proceedings of the first ACM workshop on multimedia in forensics, pp 49–54

  149. Zhang Z, Liu Q (2019) Detect video forgery by performing transfer learning on deep neural network. In: The international conference on natural computation, fuzzy systems and knowledge discovery. Springer, pp 415–422

  150. Zhang Z, Hou J, Li Z, Li D (2015a) Inter-frame forgery detection for static-background video based on mvp consistency. In: International workshop on digital watermarking. Springer, pp 94–106

  151. Zhang Z, Hou J, Ma Q, Li Z (2015b) Efficient video frame insertion and deletion detection based on inconsistency of correlations between local binary pattern coded frames. Sec Commun Netw 8(2):311–320

    Google Scholar 

  152. Zhao DN, Wang RK, Lu ZM (2018) Inter-frame passive-blind forgery detection for video shot based on similarity analysis. Multimedia Tools Appl

  153. 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. Springer, pp 18–30

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  1. Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Nitin Arvind Shelke & Singara Singh Kasana

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Shelke, N.A., Kasana, S.S. A comprehensive survey on passive techniques for digital video forgery detection. Multimed Tools Appl 80, 6247–6310 (2021). https://doi.org/10.1007/s11042-020-09974-4

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