Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32119
Video Data Mining based on Information Fusion for Tamper Detection

Authors: Girija Chetty, Renuka Biswas


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.

Keywords: image tamper detection, digital forensics, correlation features image fusion

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1750


[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",
[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.