Advertisement

Detecting Manipulations in Video

  • Grégoire Mercier
  • Foteini Markatopoulou
  • Roger Cozien
  • Markos ZampoglouEmail author
  • Evlampios Apostolidis
  • Alexandros I. Metsai
  • Symeon Papadopoulos
  • Vasileios Mezaris
  • Ioannis Patras
  • Ioannis Kompatsiaris
Chapter

Abstract

This chapter presents the techniques researched and developed within InVID for the forensic analysis of videos, and the detection and localization of forgeries within User-Generated Videos (UGVs). Following an overview of state-of-the-art video tampering detection techniques, we observed that the bulk of current research is mainly dedicated to frame-based tampering analysis or encoding-based inconsistency characterization. We built upon this existing research, by designing forensics filters aimed to highlight any traces left behind by video tampering, with a focus on identifying disruptions in the temporal aspects of a video. As for many other data analysis domains, deep neural networks show very promising results in tampering detection as well. Thus, following the development of a number of analysis filters aimed to help human users in highlighting inconsistencies in video content, we proceeded to develop a deep learning approach aimed to analyze the outputs of these forensics filters and automatically detect tampered videos. In this chapter, we present our survey of the state of the art with respect to its relevance to the goals of InVID, the forensics filters we developed and their potential role in localizing video forgeries, as well as our deep learning approach for automatic tampering detection. We present experimental results on benchmark and real-world data, and analyze the results. We observe that the proposed method yields promising results compared to the state of the art, especially with respect to the algorithm’s ability to generalize to unknown data taken from the real world. We conclude with the research directions that our work in InVID has opened for the future.

References

  1. 1.
    Qi X, Xin X (2015) A singular-value-based semi-fragile watermarking scheme for image content authentication with tamper localization. J Vis Commun Image Represent 30:312–327CrossRefGoogle Scholar
  2. 2.
    Qin C, Ji P, Zhang X, Dong J, Wang J (2017) Fragile image watermarking with pixel-wise recovery based on overlapping embedding strategy. Signal Process 138:280–293CrossRefGoogle Scholar
  3. 3.
    Shehab A, Elhoseny M, Muhammad K, Sangaiah AK, Yang P, Huang H, Hou G (2018) Secure and robust fragile watermarking scheme for medical images. IEEE Access 6:10269–10278CrossRefGoogle Scholar
  4. 4.
    Warif NBA, Wahab AWA, Idris MYI, Ramli R, Salleh R, Shamshirband S, Choo KKR (2016) Copy-move forgery detection: survey, challenges and future directions. J Netw Comput Appl 100(75):259–278CrossRefGoogle Scholar
  5. 5.
    Soni B, Das PK, Thounaojam DM (2017) CMFD: a detailed review of block based and key feature based techniques in image copy-move forgery detection. IET Image Process 12(2):167–178CrossRefGoogle Scholar
  6. 6.
    Farid H (2009) Exposing digital forgeries from JPEG ghosts. IEEE Trans Inf Forens Secur 4(1):154–160CrossRefGoogle Scholar
  7. 7.
    Iakovidou C, Zampoglou M, Papadopoulos S, Kompatsiaris Y (2018) Content-aware detection of JPEG grid inconsistencies for intuitive image forensics. J Vis Commun Image Represent 54:155–170CrossRefGoogle Scholar
  8. 8.
    Mahdian B, Saic S (2009) Using noise inconsistencies for blind image forensics. Image Vis Comput 27(10):1497–1503CrossRefGoogle Scholar
  9. 9.
    Cozzolino D, Poggi G, Verdoliva L (2015) Splicebuster: a new blind image splicing detector. In: 2015 IEEE international workshop on information forensics and security (WIFS). IEEE, pp 1–6Google Scholar
  10. 10.
    Dirik AE, Memon N (2009) Image tamper detection based on demosaicing artifacts. In: Proceedings of the 2009 IEEE international conference on image processing (ICIP 2009). IEEE, pp 1497–1500Google Scholar
  11. 11.
    Ferrara P, Bianchi T, De Rosa A, Piva A (2012) Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Trans Inf Forens Secur 7(5):1566–1577CrossRefGoogle Scholar
  12. 12.
    Cozzolino D, Gragnaniello D, Verdoliva L (2014) Image forgery detection through residual-based local descriptors and block-matching. In: 2014 IEEE international conference on image processing (ICIP). IEEE, pp 5297–5301Google Scholar
  13. 13.
    Muhammad G, Al-Hammadi MH, Hussain M, Bebis G (2014) Image forgery detection using steerable pyramid transform and local binary pattern. Mach Vis Appl 25(4):985–995CrossRefGoogle Scholar
  14. 14.
    Zhang Y, Li S, Wang S, Shi YQ (2014) Revealing the traces of median filtering using high-order local ternary patterns. IEEE Signal Process Lett 3(21):275–279CrossRefGoogle Scholar
  15. 15.
    Birajdar GK, Mankar VH (2014) Blind method for rescaling detection and rescale factor estimation in digital images using periodic properties of interpolation. AEU-Int J Electron Commun 68(7):644–652CrossRefGoogle Scholar
  16. 16.
    Vázquez-Padín D, Comesana P, Pérez-González F (2015) An SVD approach to forensic image resampling detection. In: 2015 23rd European signal processing conference (EUSIPCO). IEEE, pp 2067–2071Google Scholar
  17. 17.
    Chen J, Kang X, Liu Y, Wang ZJ (2015) Median filtering forensics based on convolutional neural networks. IEEE Signal Process Lett 22(11):1849–1853CrossRefGoogle Scholar
  18. 18.
    Bayar B, Stamm MC (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM workshop on information hiding and multimedia security. ACM, pp 5–10Google Scholar
  19. 19.
    Bayar B, Stamm MC (2017) On the robustness of constrained convolutional neural networks to JPEG post-compression for image resampling detection. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2152–2156Google Scholar
  20. 20.
    Liu Y, Guan Q, Zhao X, Cao Y (2018) Image forgery localization based on multi-scale convolutional neural networks. In: Proceedings of the 6th ACM workshop on information hiding and multimedia security. ACM, pp 85–90Google Scholar
  21. 21.
    Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forens Secur 7(3):868–882CrossRefGoogle Scholar
  22. 22.
    Cozzolino D, Poggi G, Verdoliva L (2017) Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection. In: Proceedings of the 5th ACM workshop on information hiding and multimedia security. ACM, pp 159–164Google Scholar
  23. 23.
    Zampoglou M, Papadopoulos S, Kompatsiaris Y (2017) Large-scale evaluation of splicing localization algorithms for web images. Multim Tools Appl 76(4):4801–4834CrossRefGoogle Scholar
  24. 24.
    Sitara K, Mehtre BM (2016) Digital video tampering detection: an overview of passive techniques. Digit Investig 18:8–22CrossRefGoogle Scholar
  25. 25.
    Singh R, Vatsa M, Singh SK, Upadhyay S (2009) Integrating SVM classification with svd watermarking for intelligent video authentication. Telecommun Syst 40(1–2):5–15CrossRefGoogle Scholar
  26. 26.
    Zhi-yu H, Xiang-hong T (2011) Integrity authentication scheme of color video based on the fragile watermarking. In: 2011 international conference on electronics, communications and control (ICECC). IEEE, pp 4354–4358Google Scholar
  27. 27.
    Fallahpour M, Shirmohammadi S, Semsarzadeh M, Zhao J (2014) Tampering detection in compressed digital video using watermarking. IEEE Trans Instrum Meas 63(5):1057–1072CrossRefGoogle Scholar
  28. 28.
    Tong M, Guo J, Tao S, Wu Y (2016) Independent detection and self-recovery video authentication mechanism using extended NMF with different sparseness constraints. Multim Tools Appl 75(13):8045–8069CrossRefGoogle Scholar
  29. 29.
    Sowmya K, Chennamma H, Rangarajan L (2018) Video authentication using spatio temporal relationship for tampering detection. J Inf Secur Appl 41:159–169Google Scholar
  30. 30.
    Piva A (2013) An overview on image forensics. ISRN Signal Process:1–22CrossRefGoogle Scholar
  31. 31.
    Pandey R, Singh S, Shukla K (2014) Passive copy-move forgery detection in videos. In: IEEE international conference on computer and communication technology (ICCCT), pp 301–306Google Scholar
  32. 32.
    Lin CS, Tsay JJ (2014) A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis. Digit Investig 11(2):120–140CrossRefGoogle Scholar
  33. 33.
    Su L, Huang T, Yang J (2015) A video forgery detection algorthm based on compressive sensing. Multim Tools Appl 74:6641–6656CrossRefGoogle Scholar
  34. 34.
    Dong Q, Yang G, Zhu N (2012) A MCEA based passive forensics scheme for detecting frame based video tampering. Digit Investig:151–159CrossRefGoogle Scholar
  35. 35.
    Fu D, Shi Y, Su W (2009) A generalized Benford’s law for JPEG coefficients and its applications in image forensics. In: Proceedings of SPIE, security, steganography and watermarking of multimedia contents IX, vol 6505, pp 39–48Google Scholar
  36. 36.
    Milani S, Bestagini P, Tagliasacchi M, Tubaro S (2012) Multiple compression detection for video sequences. In: MMSP. IEEE, pp 112–117. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6331800
  37. 37.
    Xu J, Su Y, liu Q (2013) Detection of double MPEG-2 compression based on distribution of DCT coefficients. Int J Pattern Recognit Artif Intell 27(1)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Wang W, Farid H (2006) Exposing digital forgery in video by detecting double MPEG compression. In: Proceedings of the 8th workshop on multimedia and security. ACM, pp 37–47Google Scholar
  39. 39.
    Su Y, Xu J (2010) Detection of double compression in MPEG-2 videos. In: IEEE 2nd international workshop on intelligent systems and application (ISA)Google Scholar
  40. 40.
    Shanableh T (2013) Detection of frame deletion for digital video forensics. Digit Investig 10(4):350–360.  https://doi.org/10.1016/j.diin.2013.10.004CrossRefGoogle Scholar
  41. 41.
    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)CrossRefGoogle Scholar
  42. 42.
    Gironi A, Fontani M, Bianchi T, Piva A, Barni M (2014) A video forensic technique for detecting frame deletion and insertion. In: ICASSPGoogle Scholar
  43. 43.
    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)Google Scholar
  44. 44.
    Papadopoulou O, Zampoglou M, Papadopoulos S, Kompatsiaris I (2018) A corpus of debunked and verified user-generated videos. Online Inf RevGoogle Scholar
  45. 45.
    Choi Y, Choi M, Kim M, Ha JW, Kim S, Choo J (2018) StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: IEEE conference on computer vision and pattern recognition (CVPR)Google Scholar
  46. 46.
    Baek K, Bang D, Shim H (2018) Editable generative adversarial networks: generating and editing faces simultaneously. CoRR. arXiv:1807.07700
  47. 47.
    Bansal A, Ma S, Ramanan D, Sheikh Y (2018) Recycle-GAN: unsupervised video retargeting. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision—ECCV 2018—15th European conference, Munich, Germany, September 8–14, 2018, Proceedings, Part V. Lecture notes in computer science, vol 11209. Springer, pp 122–138Google Scholar
  48. 48.
    Lee HY, Tseng HY, Huang JB, Singh M, Yang MH (2018) Diverse image-to-image translation via disentangled representations. In: Proceedings of the European conference on computer vision (ECCV), pp 35–51CrossRefGoogle Scholar
  49. 49.
    Wang Y, Tao X, Qi X, Shen X, Jia J (2018) Image inpainting via generative multi-column convolutional neural networks. In: Advances in neural information processing systems, pp 331–340Google Scholar
  50. 50.
    Yu J, Lin Z, Yang J, Shen X, Lu X, Huang TS (2018) Generative image inpainting with contextual attention. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5505–5514Google Scholar
  51. 51.
    Dehnie S, Sencar HT, Memon ND (2006) Digital image forensics for identifying computer generated and digital camera images. In: Proceedings of the 2006 IEEE international conference on image processing (ICIP 2006). IEEE, pp 2313–2316. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4106439
  52. 52.
    Dirik AE, Bayram S, Sencar HT, Memon ND (2007) New features to identify computer generated images. In: Proceedings of the 2007 IEEE international conference on image processing (ICIP 2007). IEEE, pp 433–436. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4378863
  53. 53.
    Wang J, Li T, Shi YQ, Lian S, Ye J (2017) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multim Tools Appl 76(22):23721–23737CrossRefGoogle Scholar
  54. 54.
    Rössler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M (2019) Faceforensics++: learning to detect manipulated facial images. arXiv:1901.08971
  55. 55.
    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. CoRR. arXiv:1803.09179v1
  56. 56.
    Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258Google Scholar
  57. 57.
    Afchar D, Nozick V, Yamagishi J, Echizen I (2018) MesoNet: a compact facial video forgery detection network. CoRR. arXiv:1809.00888
  58. 58.
    Ye S, Sun Q, Chang EC (2007) Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In: 2007 IEEE international conference on multimedia and expo. IEEE, pp 12–15Google Scholar
  59. 59.
    Mallat S (2009) A wavelet tour of signal processing, 3rd edn. Academic PressGoogle Scholar
  60. 60.
    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 374–386Google Scholar
  61. 61.
    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 (CVPR 2015), pp 1–9Google Scholar
  62. 62.
    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 (CVPR 2016), pp 770–778.  https://doi.org/10.1109/CVPR.2016.90
  63. 63.
    Pittaras N, Markatopoulou F, Mezaris V, Patras I (2017) Comparison of fine-tuning and extension strategies for deep convolutional neural networks. In: Proceedings of the 23rd international conference on multimedia modeling (MMM 2017). Springer, Reykjavik, Iceland, pp 102–114Google Scholar
  64. 64.
    Papadopoulou O, Zampoglou M, Papadopoulos S, Kompatsiaris Y, Teyssou D (2018) Invid fake video corpus v2.0 (version 2.0). Dataset on ZenodoGoogle Scholar
  65. 65.
    Yao Y, Shi Y, Weng S, Guan B (2017) Deep learning for detection of object-based forgery in advanced video. Symmetry 10(1):3CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Grégoire Mercier
    • 1
  • Foteini Markatopoulou
    • 2
  • Roger Cozien
    • 1
  • Markos Zampoglou
    • 2
    Email author
  • Evlampios Apostolidis
    • 2
    • 3
  • Alexandros I. Metsai
    • 2
  • Symeon Papadopoulos
    • 2
  • Vasileios Mezaris
    • 2
  • Ioannis Patras
    • 3
  • Ioannis Kompatsiaris
    • 2
  1. 1.eXo maKinaParisFrance
  2. 2.Information Technologies InstituteCentre for Research and Technology HellasThessalonikiGreece
  3. 3.School of Electronic Engineering and Computer ScienceQueen Mary UniversityLondonUK

Personalised recommendations