Abstract
With regard to the Copy-Move forgery of image region, this paper proposes one blind detection algorithm based on Multi-Scale Autoconvolution. The eigenvectors of image are generated by extracting the MSA invariants of each image block, and sorted by dictionary ordering. Then the similarity of image blocks are computed by using correlation coefficients in order to detect and locate the tampered image regions. The experimental results show that the algorithm can not only effectively detect and locate the duplication regions, but also resist part of post-processing operations including rotation attack, Gaussian noises attack and JPEG compression attack. It demonstrates that our proposed algorithm has advantages of low time complexity and high robustness.
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References
Farid, H.: Photo fakery and forensics. Advanced Computation 77, 1–55 (2009)
Wang, S.Z., Wu, Q.M.: Is it Fake?—Image Authentication and Forgery Detection. Chinese Journal of Natural 32(5), 281–287 (2010)
Cox, I.J., Kilian, J., Leighton, F.T.: Secure spread spectrum watermarking for multimedia. IEEE Transactions on Image Processing 12(6), 1673–1687 (1997)
Swaminathan, A., Mao, Y., Wu, M.: Robust and secure image hashing. IEEE Transactions on Information Forensics and Security 1(2), 215–230 (2006)
Luo, W.Q., Qu, Z.H., Pan, F., et al.: A survey of passive technology for digital image forensics. Frontiers of Computer Science in China 1(2), 166–179 (2007)
Fridrich, J., Soukal, D., Lukas, J.: Detection of copymove forgery in digital images. In: Proceedings of the Digital Forensic Research Workshop, Cleveland OH, USA (2003)
Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting duplicated image regions. Dartmouth College, Hanover (2004) TR2004-515
Wang, J.W., Liu, G.J., Zhang, Z., et al.: Fast and robust forensics for image region-duplication forgery. Acta Automatica Sinica 35(12), 1488–1495 (2009)
Cao, Y.J., Gao, T.G., Fan, L., Yang, Q.T.: A robust detection algorithm for copy-move forgery in digital images. Forensic Science International 214(1), 33–43 (2011)
Rahtu, E., Salo, M., Heikkilä, J.: Affine invariant pattern recognition using Multiscale Autoconvolution. IEEE Transactions on PAMI 27(6), 908–918 (2005)
Rahtu, E., Salo, M., Heikkilä, J.: A new convexity measure based on a probabilistic interpretation of images. IEEE Transactions on PAMI 28(9), 1501–1512 (2006)
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© 2012 Springer-Verlag Berlin Heidelberg
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Wang, T., Tang, J., Zhao, W., Xu, Q., Luo, B. (2012). Blind Detection of Copy-Move Forgery Based on Multi-Scale Autoconvolution Invariants. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_54
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DOI: https://doi.org/10.1007/978-3-642-33506-8_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
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