Video Data Mining based on Information Fusion for Tamper Detection

In this paper, we propose novel algorithmic models based on information fusion and feature transformation in crossmodal subspace for different types of residue features extracted from several intra-frame and inter-frame pixel sub-blocks in video sequences for detecting digital video tampering or forgery. An evaluation of proposed residue features – the noise residue features and the quantization features, their transformation in cross-modal subspace, and their multimodal fusion, for emulated copy-move tamper scenario shows a significant improvement in tamper detection accuracy as compared to single mode features without transformation in cross-modal subspace.

Authors:

• Girija Chetty
• Renuka Biswas

Keywords:

• image tamper detection
• digital forensics
• correlation features image fusion

References:

[1] S. Bayram, H. T. Sencar, and N. Memon, An Efficient and Robust
Method For Detecting Copy-Move Forgery. IEEE International
Conference on Acoustics, Speech and Signal Processing, ICASSP 2009,
Taipei Taiwan, June 2009.
[2] A. E. Dirik and N. Memon, Image Tamper Detection Based on
Demosaicing Artifacts. IEEE ICIP 09, November 2009, Cairo Egypt.
[3] Alin C .Popescu and Hany Farid, "Exposing Digital Forgeries by
Detecting Traces of Re-sampling",IEEE Transactions on signal
processing ,Vol. 53,No.2,February 2005 .
[4] Jessica Fridrich, David Sukal and Jan Lukas, "Detection of Copy-Move
Forgery in Digital Images",
http://www.ws.binghamton.edu/fridrich/Research/copymove.pdf
[5] Y. F. Hsu and S. -F. Chang. "Detecting Image Splicing Using Geometry
Invariants and Camera Characteristics Consistency", In ICME, Toronto,
Canada, July 2006.
[6] Y. Q. Shi, C. Chen, and W. Chen, "A natural image model approach to
splicing detection," in Proc. ACM Multimedia Security Workshop, pp.
51-62, Sept. 2007, Dallas, Texas.
[7] H. Gou, A. Swaminathan, and M. Wu, "Noise Features for Image
Tampering Detection and Steganalysis," Proc. of IEEE Int. Conf. On
Image Processing (ICIP'07), San Antonio, TX, Sept. 2007.
[8] T. T. Ng, S. -F. Chang, C. -Y. Lin, and Q. Sun, "Passive-blind Image
Forensics", In Multimedia Security Technologies for Digital Rights, W.
Zeng, H. Yu, and C. -Y. Lin (eds.), Elsvier, 2006.
[9] A. Criminisi, P Perez, and K. Toyama, "Region filling and object
removal by exemplar-based image inpainting," IEEE Trans. Image
Process., vol.13, no.9, pp. 1200-1212, Sept. 2004
[10] Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., &
Harshman, R. (1990). Indexing by latent semantic analysis. Journal of
the American Society for Information Science, 41(6), 391-407.
[11] M. Borga and H. Knutsson, "Finding Efficient Nonlinear Visual
Operators using Canonical Correlation Analysis, " in Proc. of SSAB-
2000, Halmstad, pp. 13-16.
[12] Sanderson, C. and K.K. Paliwal , "Fast features for face authentication
under illumination direction changes", Pattern Recognition Letters 24,
2409-2419, 2003.
[13] M. K. Mihcak, I. Kozintsev, and K. Ramchandran, "Spatially adaptive
statistical modeling of wavelet image coefficients and its application to
denoising," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal
Processing, vol. 6, pp. 3253-3256, Mar. 1999, Phoenix, AZ
[14] Y.Sun, Y.Shi, F.Chen, V.Chung, "Skipping Spare Information in
Multimodal Inputs during Multimodal Input Fusion", Proceeding of the
2009 International Conference on Intelligent User Interfaces, IUI2009,
Sanibel Island, Florida, USA, 8-11 February 2009.