Abstract
Nowadays, people can easily combine several videos into a fake one by means of matte painting to create visually convincing video contents. This raises the need to verify whether a video content is original or not. In this paper we propose a geometric technique to detect this kind of tampering in video sequences. In this technique, the extrinsic camera parameters, which describe positions and orientations of camera, are estimated from different regions in video frames. A statistical distribution model is then developed to characterize these parameters in tampering-free video and provides evidences of video forgery finally. The efficacy of the proposed method has been demonstrated by experiments on both authentic and tampered videos from websites.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Milani, S., Fontani, M., Bestagini, P., Barni, M., Piva, A., Tagliasacchi, M., Tubaro, S.: An overview on video forensics. APSIPA Trans. Sig. Inf. Process. 1, e2 (2012). Cambridge Univ Press, Cambridge
Wang, W., Farid, H.: Exposing digital forgeries in interlaced and deinterlaced video. IEEE Trans. Inf. Forensics Secur. 2(3), 438–449 (2007). IEEE Press, New York
Stamm, M.C., Lin, W.S., Liu, K.J.: Temporal forensics and anti-forensics for motion compensated video. IEEE Trans. Inf. Forensics Secur. 7(4), 1315–1329 (2012). IEEE Press, New York
Hsu, C.C., Hung, T.Y., Lin, C.W., Hsu, C.T.: Video forgery detection using correlation of noise residue. In: 2008 IEEE 10th Workshop on In Multimedia Signal Processing, pp. 170–174. IEEE Press, New York(2008)
Chen, M., Fridrich, J., Goljan, M., Lukas, J.: Determining image origin and intergrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3(1), 74–90 (2008). IEEE Press, New York
Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of the 7th Workshop on Multimedia and Security, pp. 1–10. ACM (2005)
Kee, E., O’Brien, J.F., Farid, H.: Exposing photo manipulation with inconsistent shadows. ACM Trans. Graph. 32(4), 28 (2013). 1C-12. ACM
O’Brien, J.F., Farid, H.: Exposing photo manipulation with inconsistent reflections. ACM Trans. Graph. 31(1), 4 (2012). 1C-11. ACM
Wang, W., Farid, H.: Exposing digital forgeries in video by detecting duplication. In: Proceedings of the 9th Workshop on Multimedia and Security, pp. 35–42. ACM (2007)
Yao, H., Wang, S.: Detecting image forgery using perspective constraints. Signal Process. Lett. 19(3), 123–126 (2012). IEEE Press, New York
Wang, W., Farid, H.: Detecting Re-projected Video. Proceedings of International Workshop on Information Hiding. Springer, Heidelberg (2008)
Conotter, V., Boato, G., Farid, H.: Detecting photo manipulation on signs and billboards. In: 2010 17th IEEE International Conference on Image Processing, pp. 1741–1744. IEEE Press, New York (2010)
Zhang, W., Cao, X., Qu, Y., Hou, Y., Zhao, H., Zhang, C.: Detecting and extracting the photo composites using planar homography and graph cut. IEEE Trans. Inf. Forensics Secur. 5(3), 544–555 (2010). IEEE Press, New York
Zhang, Z.: Flexible camera calibration by viewing a plane from unknown orientations. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 666–673 (2010)
Nister, D.: An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 756–770 (2004). IEEE Press, New York
Hartley, R.: In defense of the eight-point algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 19(6), 580–C593 (1997). IEEE Press, New York
Johnson, M.K., Farid, H.: Detecting photographic composites of people. In: Proceedings of International Workshop on Digital Watermarking, pp. 19–33. Springer, Heidelberg (2008)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 2(66), 91–110 (2004). Springer, Heidelberg
Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysisand automated cartography. Commun. ACM 24(6), 381–395 (1981). ACM
Wu, C.: Towards linear-time incremental structure from motion. In: 2013 International Conference on 3D Vision-3DV 2013, pp. 127–134 (2013)
Wu, C.: VisualSFM: A Visual Structure from Motion System. http://ccwu.me/vsfm/
Wu, C., Agarwal, S., Curless, B., Seitz, S.M.: Multicore bundle adjustment. In: IEEE Conference on Computer Vision and Pattern Recognition, pp: 3057–3064. IEEE Press, New York (2011)
Acknowledgment
The authors appreciate the supports received from National Natural Science Foundation of China (No. 61379156 and 60970145), the National Research Foundation for the Doctoral Program of Higher Education of China (No. 20120171110-037) and the Key Program of Natural Science Foundation of Guangdong (No. S2012020011114).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Hu, X., Ni, J., Pan, R. (2016). Detecting Video Forgery by Estimating Extrinsic Camera Parameters. In: Shi, YQ., Kim, H., Pérez-González, F., Echizen, I. (eds) Digital-Forensics and Watermarking. IWDW 2015. Lecture Notes in Computer Science(), vol 9569. Springer, Cham. https://doi.org/10.1007/978-3-319-31960-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-31960-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31959-9
Online ISBN: 978-3-319-31960-5
eBook Packages: Computer ScienceComputer Science (R0)