Abstract
This paper presents a method for detecting the removed object in video captured by stationary camera. The method is based on an observation that the removed object, while not distinguishable by human eyes, leaves artifacts that can be detected by computers. In this paper, the block based motion estimation method is employed to extract motion information from adjacent video frames. Then the magnitude and orientation of the motion vectors are used to differentiate the authentic region and the forged region. By exploring the discrepancies in motion vectors, the position of the removed object can be revealed. The efficiency of the proposed method is demonstrated by experiments.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Farid, H.: A survey of image forgery detection. IEEE Signal Processing Magnazine 26(2), 16–25 (2009)
Wang, W.H., Farid, H.: Exposing digital forgeries in video by detecting duplication. In: Proc. MMSec 2007, ACM Multimedia and Security Workshop, pp. 35–42 (2007)
Wang, W.H., Farid, H.: Exposing digital forgeries in interlaced and de-interlaced video. IEEE Transactions on Information Forensics and Security 2(3), 438–449 (2007)
Wang, W., Farid, H.: Detecting re-projected video. In: Solanki, K., Sullivan, K., Madhow, U. (eds.) IH 2008. LNCS, vol. 5284, pp. 72–86. Springer, Heidelberg (2008)
Wang, W.H., Farid, H.: Exposing digital forgeries in video by detecting double MPEG compression. In: Proc. MMSec 2006, ACM Multimedia and Security Workshop, pp. 37–47 (2006)
Chen, W., Shi, Y.Q.: Detection of double MPEG compression based on first digit statistics. In: Kim, H.-J., Katzenbeisser, S., Ho, A.T.S. (eds.) IWDW 2008. LNCS, vol. 5450, pp. 16–30. Springer, Heidelberg (2009)
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)
Kobayashi, M., Okabe, T., Sato, Y.: Detecting forgery from static-scene video based on inconsistency in noise level functions. IEEE Transactions on Information Forensics and Security 5(4), 883–892 (2010)
Su, Y.T., Zhang, J., Liu, J.: Exposing digital video forgery by detecting motion-compensated edge artifact. In: Proc. CiSE 2009, International Conference on Computational Intelligence and Software Engineering, pp. 1–4 (2009)
Zhang, J., Su, Y.T., Zhang, M.Y.: Exposing digital video forgery by ghost shadow artifact. In: Proc. MiFor 2009, ACM Workshop on Multimedia in Forensics, pp. 49–53 (2009)
Su, Y.T., Zhang, J., Han, Y., Chen, J.: Exposing digital video logo-removal forgery by inconsistency of blur. International Journal of Pattern Recognition and Artificial Intelligence 24(7), 1027–1046 (2010)
Chetty, G.: Blind and passive digital video tamper detection based on multimodal fusion. In: Proc. of the 14th WSEAS International Conference on Communications, pp. 109–117 (2010)
Chetty, G., Biswas, M., Singh, R.: Digital video tamper detection based on multimodal fusion of residue features. In: Proc. International Conference on Network and System Security, pp. 606–613 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, L., Wang, X., Zhang, W., Yang, G., Hu, G. (2013). Detecting Removed Object from Video with Stationary Background. In: Shi, Y.Q., Kim, HJ., Pérez-González, F. (eds) The International Workshop on Digital Forensics and Watermarking 2012. IWDW 2012. Lecture Notes in Computer Science, vol 7809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40099-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-40099-5_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40098-8
Online ISBN: 978-3-642-40099-5
eBook Packages: Computer ScienceComputer Science (R0)