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Copy-move tampering detection using affine transformation property preservation on clustered keypoints

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Abstract

Recent advances in multimedia technologies have made imaging devices and image editing tools ubiquitous and affordable. Image editing done with malicious intent is called as image tampering or forgery. The most common forgery is the copy-move forgery which involves copying a part of an image and pasting it on some other part of the same image. There are many existing methods for such forgery detection, but most of them are sensitive to post-processing and do not detect multiple instances of forgeries in an image. In the proposed approach, affine transformation property preservation of clustered keypoints in the image is used, which includes the tests for collinearity and distance ratio preservation. Our method is also able to detect multiple copy-move forgeries within an image. The proposed method is tested against four image tampering detection datasets, and the results of our method are the best compared to the existing eight state-of-the-art methods in terms of accuracy.

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References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels. Technical Report 149300, EPFL (2010)

  2. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)

    Article  Google Scholar 

  3. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G.: A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(3), 1099–1110 (2011)

    Article  Google Scholar 

  4. Ardizzone, E., Bruno, A., Mazzola, G.: Detecting multiple copies in tampered images. In: 2010 IEEE International Conference on Image Processing, pp. 2117–2120 (2010). doi:10.1109/ICIP.2010.5652490

  5. Ardizzone, E., Bruno, A., Mazzola, G.: Copy-move forgery detection by matching triangles of keypoints. IEEE Trans. Inf. Forensics Secur. 10(10), 2084–2094 (2015)

    Article  Google Scholar 

  6. Bayram, S., Sencar, H.T., Memon, N.: An efficient and robust method for detecting copy-move forgery. In: ICASSP 2009. IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1053–1056. IEEE (2009)

  7. Cao, Y., Gao, T., Fan, L., Yang, Q.: A robust detection algorithm for copy-move forgery in digital images. Forensic Sci. Int. 214(1), 33–43 (2012)

    Article  Google Scholar 

  8. Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)

    Article  Google Scholar 

  9. Cozzolino, D., Poggi, G., Verdoliva, L.: Efficient dense-field copy-move forgery detection. IEEE Trans. Inf. Forensics Secur. 10(11), 2284–2297 (2015)

    Article  Google Scholar 

  10. Fridrich, A.J., Soukal, B.D., Luk, A.J.: Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop (2003)

  11. Huang, H., Guo, W., Zhang, Y.: Detection of copy-move forgery in digital images using SIFT algorithm. In: Pacific-Asia Workshop on Computational Intelligence and Industrial Application PACIIA ’08, vol. 2, pp. 272–276 (2008). doi:10.1109/PACIIA.2008.240

  12. Juan, L., Gwun, O.: A comparison of SIFT, PCA-SIFT and SURF. Int. J. Image Process. (IJIP) 3(4), 143–152 (2009)

    Google Scholar 

  13. Li, L., Li, S., Zhu, H., Chu, S.C., Roddick, J.F., Pan, J.S.: An efficient scheme for detecting copy-move forged images by local binary patterns. J. Inf. Hiding Multimed. Signal Process. 4(1), 46–56 (2013)

    Google Scholar 

  14. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  15. Mahmood, T., Nawaz, T., Ashraf, R., Shah, M., Khan, Z., Irtaza, A., Mehmood, Z.: A survey on block based copy move image forgery detection techniques. In: 2015 International Conference on Emerging Technologies (ICET), pp. 1–6 (2015). doi:10.1109/ICET.2015.7389169

  16. Manu, V.T., Mehtre, B.M.: Detection of copy-move forgery in images using segmentation and SURF. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, K.C., Mosin, S., Ma, M. (eds.) Advances in Signal Processing and Intelligent Recognition Systems, pp. 645–654. Springer, Berlin (2016)

  17. Panchal, P., Panchal, S., Shah, S.: A comparison of SIFT and SURF. Int. J. Innov. Res. Comput. Commun. Eng. 1(2), 323–327 (2013)

    Google Scholar 

  18. Tralic, D., Zupancic, I., Grgic, S., Grgic, M.: Comofodnew database for copy-move forgery detection. In: 2013 55th International Symposium on ELMAR, pp. 49–54. IEEE (2013)

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Manu, V.T., Mehtre, B.M. Copy-move tampering detection using affine transformation property preservation on clustered keypoints. SIViP 12, 549–556 (2018). https://doi.org/10.1007/s11760-017-1191-7

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  • DOI: https://doi.org/10.1007/s11760-017-1191-7

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