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A novel video forgery detection algorithm for blue screen compositing based on 3-stage foreground analysis and tracking

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Abstract

Blue screen compositing is one of the most common methods to do video forgery. However, few algorithms have been proposed to detect the forgery in this form. This paper presents a 3-stage Foreground Analysis and Tracking algorithm (3FAT) to detect blue screen compositing. The 3FAT algorithm contains three major stages: foreground block extraction, forged block detection and forged block tracking. The first stage extracts the foreground blocks by a multi-pass foreground locating method. In the second stage, a feature-comparison level fusion of local features consisting of luminance and contrast is put forward to seek out the tampered foreground block. In the last stage, a fast target search algorithm based on Compressive Tracking is used to track the tampered block of subsequent frames. Compared with previous algorithm, 3FAT can not only rule out the distractions of noise and other moving foregrounds, but also be applied to any video format, bit rate and encoding mechanism. The experiments show that the 3FAT algorithm has higher accuracy and performs well in terms of speed.

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References

  1. Bagiwa MA, Wahab AWA et al (2016) Chroma key background detection for digital video using statistical correlation of blurring artifact. Digit Investig 19:29–43

    Article  Google Scholar 

  2. Bidokhti A, Ghaemmaghami S (2015) Detection of regional copy/move forgery in MPEG videos using optical flow. Artificial Intelligence and Signal Processing (AISP), 2015 International Symposium on

  3. Candes EJ, Tao T (2006) Near optimal signal recovery from random projections: universal encoding strategies. IEEE Trans Inf Theory 52(12):5406–5425

    Article  MathSciNet  MATH  Google Scholar 

  4. Chen W, Yang G, Chen R, Zhu N (2011) Digital video passive forensics for its authenticity and source. J Commun 32(6):77–182

    Google Scholar 

  5. GB Chittapur et al (2014) Exposing digital forgery in video by mean frame comparison techniques. Emerging Research in Electronics, Computer Science and Technology. Springer India, 557–562

  6. D’Amiano L et al, (2015) Video forgery detection and localization based on 3D patchmatch. Multimedia & Expo Workshops (ICMEW), 2015 I.E. International Conference on. IEEE

  7. Diaconis P, Freedman D (1984) Asymptotics of graphical projection pursuit. Ann Stat:793–815

  8. Dibyendu M (2013) Multiresolution based Gaussian mixture model for background suppression. IEEE Trans Image Process 22(12):5022–5035

    Article  MathSciNet  MATH  Google Scholar 

  9. Felzenszwalb PF, Huttenlocher DP (2004) Efficient Graph-based image segmentation. Int J Comput Vis 59(2):167–181

    Article  Google Scholar 

  10. Jordan A (2002) On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. Adv Neural Inf Proces Syst 14(1):841

    Google Scholar 

  11. Kobayashi M, Okabe T, Sato Y (2009) Detecting video forgeries based on noise characteristics, Advances in Image and Video Technology. Springer, 306–317

  12. Li F, Huang T (2013) Video copy-move forgery detection and localization based on structural similarity. In: Farag A, Yang J, Jiao F (eds) Proceedings of the 3rd International Conference on Multimedia Technology (ICMT 2013). Lecture Notes in Electrical Engineering, Springer, Berlin, Heidelberg, vol 278, pp. 63–76

  13. Otsu N (1979) A threshold selection method from gray level histogram. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  MathSciNet  Google Scholar 

  14. Porter T, Duff T (1984) Compositing digital images, Computer Graphics Proceedings, Annual Conference Series. ACM SIG-GRAPH, New York: 253–259

  15. Shujia Y, Lijun J, Shaohui D, Ling Z, Chunyu Y, Wenhao Z (2012) Power line image segmentation and extra matter recognition based on improved Otsu algorithm. IET Image Process 6(4):426–433

    Article  MathSciNet  Google Scholar 

  16. Smith AR, Blinn JF (1996) Blue screen matting. Computer Graphics and Interactive Techniques:259–268

  17. Stauffer C, Grimson W (1999) Adaptive background mixture models for real-time tracking, 1999 I.E. computer society Conference on computer vision and. Pattern Recogn 2(3):246–252

    Google Scholar 

  18. Su Y, Han Y, Zhang C (2011) Detection of blue screen based on Edge Features, Information Technology and Artificial Intelligence Conference (ITAIC), 2011 6th IEEE Joint International. 469–472

  19. Subramanyam AV, Emmanuel S (2012) Video forgery detection using HOG features and compression properties, 2012 I.E. 14th International Workshop on Multimedia Signal Processing (MMSP). 89–94

  20. Vincent L (1994) Fast opening functions and morphological granulometries, Conference on Image Algebra and Morphological Image Processing. 253–267

  21. Wang W, Farid H (2006) Exposing digital forgeries in video by detecting double MPEG compression, Proceedings of the 8th workshop on Multimedia and security. ACM 37–47

  22. Wang W, Farid H (2007) Exposing digital forgeries in interlaced and deinterlaced video. IEEE Transactions on Information Forensics and Security 2(3):438–449

    Article  Google Scholar 

  23. Wang W, Farid H (2007) Exposing digital forgeries in video by detecting duplication, Proceedings of the 9th workshop on Multimedia & security. ACM 35–42

  24. Wang W, Farid H (2009) Exposing digital forgeries in video by detecting double quantization, Proceedings of the 11th ACM workshop on Multimedia and security. ACM 39–48

  25. Wang Z, Bovik AC, Sheikh HR (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  26. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  27. Xu J, Yu Y, Su Y, Dong B, You X, Detection of Blue Screen Special Effects in Videos (2012) International Conference on medical physics and biomedical engineering. Phys Procedia 33:1316–1322

    Article  Google Scholar 

  28. Zhang K, Zhang L (2012) Real-time compressive tracking. Computer Vision - ECCV 2012:864–877

    Google Scholar 

  29. Zhang J, Su Y, Zhang M (2009) Exposing digital video forgery by ghost shadow artifact, Proceedings of the First ACM workshop on Multimedia in forensics. ACM 49–54

  30. Zhou L, Wang D (2008) Digital image forensics. Beijing University of Posts and Telecommunications Press, Beijing, pp 8–13

    Google Scholar 

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Correspondence to Tianqiang Huang.

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National Natural Science Foundation of China (Grant No. 61070062). The Hundreds of Young Teachers of Climbing Project of Longyan University (Grant No. LQ2016005, LQ2015031).

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Liu, Y., Huang, T. & Liu, Y. A novel video forgery detection algorithm for blue screen compositing based on 3-stage foreground analysis and tracking. Multimed Tools Appl 77, 7405–7427 (2018). https://doi.org/10.1007/s11042-017-4652-7

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