Abstract
In this paper, we propose a novel formulation involving fusion of noise and quantization residue features for detecting tampering or forgery in video sequences. We reiterate the importance of feature selection techniques in conjunction with fusion to enhance the tamper detection accuracy. We examine three different feature selection techniques, the independent component analysis (ICA), fisher linear discriminant analysis (FLD) and canonical correlation analysis (CCA) for achieving a more discriminate subspace for extracting tamper signatures from quantization and noise residue features. The evaluation of proposed residue features, the feature selection techniques and their subsequent fusion for copy-move tampering emulated on low bandwidth Internet video sequences, show a significant improvement in tamper detection accuracy with fusion formulation.
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References
Bayram, S., Sencar, H.T., Memon, N.: An Efficient and Robust Method For Detecting Copy-Move Forgery. In: Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009, Taiwan (June 2009)
Dirik, A.E., Memon, N.: Image Tamper Detection Based on Demosaicing Artifacts. In: Proceedings IEEE ICIP Cairo, Cairo Egypt, November 09 (2009)
Popescu, A.C., Farid, H.: Exposing Digital Forgeries by Detecting Traces of Re-sampling. IEEE Transactions on signal processing 53(2) (February 2005)
Fridrich J., David, S., Jan, L.: Detection of Copy-Move Forgery in Digital Images, http://www.ws.binghamton.edu/fridrich/Research/copymove.pdf
Hsu, Y.F., Chang, S.F.: Detecting Image Splicing Using Geometry Invariants and Camera Characteristics Consistency. In: ICME, Toronto, Canada (July 2006)
Shi, Y.Q., Chen, C., Chen, W.: A natural image model approach to splicing detection. In: Proc. ACM Multimedia Security Workshop, Dallas, Texas, pp. 51–62 (September 2007)
Gou, H., Swaminathan, A., Wu, M.: Noise Features for Image Tampering Detection and Steganalysis. In: Proc. of IEEE Int. Conf. On Image Processing (ICIP 2007), San Antonio, TX (September 2007)
Ng, T.T., Chang, C.S.F., Lin, Y., Sun, Q.: Passive-blind Image Forensics. In: Zeng, W., Yu, H., Lin, C.Y. (eds.) Multimedia Security Technologies for Digital Rights. Elsevier, Amsterdam (2006)
Criminisi, A., Perez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 13(9), 1200–1212 (2004)
Gopi, E.S., Lakshmanan, N., Gokul, T., KumaraGanesh, S., Shah, P.R.: Digital Image Forgery Detection using Artificial Neural Network and Auto Regressive Coefficients. In: Proceedings Canadian Conference on Electrical and Computer Engineering, Ottawa, Canada, May 7-10, pp. 194–197 (2006)
Hsu C., Hung T., Lin C., Hsu C.: Video Forgery Detection Using Correlation of Noise Residues, www.ee.nthu.edu.tw/~cwlin/pub/mmsp08forensics.pdf (retrieved on 11/3/2010)
Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001), http://www.ece.osu.edu/~aleix/pami01.pdf
Borga, M., Knutsson, H.: Finding Efficient Nonlinear Visual Operators using Canonical Correlation Analysis. In: Proc. of SSAB 2000, Halmstad, pp. 13–16 (2000)
Laptev, I., Marszałek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: Proc. CVPR 2008, Anchorage, USA (2008)
Chetty, G., Wagner, M.: Robust face-voice based speaker identity verification using multilevel fusion. Image and Vision Computing 26(9), 1249–1260 (2008)
Hyvarinen, A., And Oja, E.: Independent Component Analysis: Algorithms and Applications. Neural Networks 13(4-5), 411–430 (2000)
Farid, H., Lyu, S.: Higher-Order Wavelet Statistics and their Application to Digital Forensics. In: IEEE Workshop on Statistical Analysis in Computer Vision, Madison, WI (2003)
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Chetty, G., Goodwin, J., Singh, M. (2010). Digital Image Tamper Detection Based on Multimodal Fusion of Residue Features. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17691-3_8
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DOI: https://doi.org/10.1007/978-3-642-17691-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17690-6
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