Abstract
Exposing digital forgeries by detecting local correlation patterns of images has become an important kind of approach among many others to establish the integrity of digital visual content. However, this kind of method is sensitive to JPEG compression, since compression attenuates the characteristics of local correlation pattern introduced by color filter array (CFA) interpolation. Rather than concentrating on the differences between image textures, we calculate the posterior probability map of CFA interpolation with compression related Gaussian model. Thus our approach will automatically adapt to compression. Experimental results on 1000 tampered images show validity and efficiency of the proposed method.
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References
Farid, H.: A survey of image forgery detection. IEEE Signal Processing Magazine 2, 16–25 (2009)
Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting duplicated image regions. Dartmouth College Tech. Rep., TR2004-515 (2004)
Fridrich, J., Soukal, D., Lukáš, J.: Detection of Copy-Move Forgery in Digital Images. In: Proceedings of Digital Forensic Research Workshop (2003)
Hsu, Y.F., Chang, S.F.: Image splicing detection using camera response function consistency and automatic segmentation. In: IEEE International Conference on Multimedia and Expo, pp. 28–31 (2007)
Luka, J., Fridrich, J., Goljan, M.: Detecting digital image forgeries using sensor pattern noise. In: Proceedings of SPIE, vol. 6072, pp. 362–372 (2006)
Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting traces of resampling. IEEE Transactions on Signal Processing 53, 758–767 (2005)
Popescu, A.C., Farid, H.: Exposing digital forgeries in color filter array interpolated images. IEEE Transactions on Signal Processing 53, 3948–3959 (2005)
He, J., Lin, Z., Wang, L., Tang, X.: Detecting doctored JPEG images via DCT coefficient analysis. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 423–435. Springer, Heidelberg (2006)
Farid, H.: Exposing digital forgeries from JPEG ghosts. IEEE Transactions on Information Forensics and Security 4, 154–160 (2009)
Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: The 7th Workshop on Multimedia and Security, pp. 1–10 (2005)
Johnson, M.K., Farid, H.: Exposing digital forgeries in complex lighting environments. IEEE Transactions on Information Forensics and Security 2, 450–461 (2007)
Gallagher, A.C., Chen, T.: Image authentication by detecting traces of demosaicing. In: CVPRW 2008 (2008)
Dirik, A.E., Memon, N.: Image tamper detection based on demosaicing artifacts. In: ICIP 2009, pp. 1497–1500 (2009)
Kirchner, M.: Efficient Estimation of CFA Pattern Configuration in Digital Camera Images. In: Proceedings of SPIE, the International Society for Optical Engineering (2010)
Kirchner, M.: Columbia photographic images and photorealistic computer graphics dataset. Columbia University, ADVENT Technical Report, 205–2004 (2004)
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Li, L., Xue, J., Wang, X., Tian, L. (2011). A Robust Approach to Detect Tampering by Exploring Correlation Patterns. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23678-5_61
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DOI: https://doi.org/10.1007/978-3-642-23678-5_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23677-8
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