International Conference on Image Analysis and Processing

ICIAP 2015: Image Analysis and Processing — ICIAP 2015 pp 665-675 | Cite as

A Tool to Support the Creation of Datasets of Tampered Videos

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)

Abstract

Digital Video Forensics is getting a growing interest from the Multimedia research community, as the need for methods to validate the authenticity of a video content is increasing with the number of videos freely available to the digital users. Unlike Digital Image Forensics, to our knowledge, there are not standard datasets to test video forgery detection techniques. In this paper we present a new tool to support the users in creating datasets of tampered videos. We furthermore present our own dataset and we discuss some remarks about how to create forgeries difficult to be detected by an observer, to the naked eye.

Keywords

Copy move forgery Video forensics Object tracking 

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References

  1. 1.
    Sencar, H.T., Memon, N.: Overview of State-Of-The-Art in Digital Image Forensics. Algorithms, Architectures and Information Systems Security 3, 325–348 (2008)CrossRefGoogle Scholar
  2. 2.
    Rocha, A., Scheirer, W., Boult, T., Goldenstein, S.: Vision of the Unseen: Current Trends and Challenges in Digital Image and Video Forensics. ACM Comput. Surv. 43(4), 42 (2011). Article 26CrossRefGoogle Scholar
  3. 3.
    Milani, S., Fontani, M., Bestagini, P., Barni, M., Piva, A., Tagliasacchi, M., Tubaro, S.: An Overview on Video Forensics. APSIPA Transactions on Signal and Information Processing 1, e2 (2012). (18 pages)CrossRefGoogle Scholar
  4. 4.
    Lee, S.J., Jung, S.H.: A survey of watermarking techniques applied to multimedia. In: Proc. IEEE Int. Symp. Industrial Electronics, vol. 1, pp. 272–277 (2001)Google Scholar
  5. 5.
    Kobayashi, M., Okabe, T., Sato, Y.: Detecting video forgeries based on noise characteristics. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 306–317. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Liao, D.D., Yang, R., Liu, H.M., et al.: Double H.264/AVC compression detection using quantized nonzero AC coefficients. In: Conference on Media Watermarking, Security, and Forensics, San Francisco, CA, vol. 7880, Article number: 78800Q (2011)Google Scholar
  7. 7.
    Sun, T., Wang, W., Jiang, X.: Exposing video forgeries by detecting MPEG double compression. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1389–1392. IEEE, March 2012Google Scholar
  8. 8.
    Wang, W., Farid, H.: Exposing digital forgeries in video by detecting duplication: In: Proc. Workshop on Multimedia & Security Int. Multimedia Conf., New York, NY, pp. 35–42 (2007)Google Scholar
  9. 9.
    Upadhyay, S., Singh, S.K.: Video Authentication: Issues and Challenges. International Journal of Computer Science Issues 9(1), 409–418 (2012). No. 3Google Scholar
  10. 10.
    Malekesmaeili, M., Fatourechi, M., Ward, R.K.: Video copy detection using temporally informative representative images. In: Proc. International Conference on Machine Learning and Applications (ICMLA 2009), pp. 69–74, December 13–15, 2009Google Scholar
  11. 11.
    Chao, J., Jiang, X., Sun, T.: A novel video inter-frame forgery model detection scheme based on optical flow consistency. In: Shi, Y.Q., Kim, H.-J., Pérez-González, F. (eds.) IWDW 2012. LNCS, vol. 7809, pp. 267–281. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Ardizzone, E., Mazzola, G.: Detection of duplicated regions in tampered digital images by bit-plane analysis. In: Foggia, P., Sansone, C., Vento, M. (eds.) ICIAP 2009. LNCS, vol. 5716, pp. 893–901. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Ardizzone, E., Bruno, A., Mazzola, G.: Copy-move forgery detection via texture description. In: Proceedings of the 2nd ACM workshop on Multimedia in Forensics, Security and Intelligence (MiFor 2010), pp. 59–64Google Scholar
  14. 14.
    Ardizzone, E., Bruno, A., Mazzola, G.: Detecting multiple copies in tampered images. In: International Conference on Image Processing, pp. 2117–2120 (2010)Google Scholar
  15. 15.
    Ardizzone, E., Bruno, A., Mazzola, G.: Copy-move forgery detection by matching triangles of keypoints. IEEE Transactions on Information Forensics and Security (2015, in press)Google Scholar
  16. 16.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comunications of the ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  19. 19.
    Hsu, C.T., Wu, J.L.: Multiresolution Mosaic. IEEE Transactions on Consumer Electronics 42(4), 981–990 (1996)CrossRefGoogle Scholar
  20. 20.
  21. 21.
  22. 22.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica (DICGIM)Università degli Studi di PalermoPalermoItaly

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