In this paper, an effective method for copy-move-rotation forgery detection is proposed which uses Zernike moments and local binary pattern (LBP) as feature extractors. First image is divided into overlapped blocks in which Zernike moments are calculated by rotating block pixels into different directions. Then rotated block with minimum value of Zernike moments is evaluated for which LBP features are extracted. Similar procedure is followed for all blocks. For matching process, mean value of block pixels is used after sorting them in an array. For similar mean value blocks, matching process is carried out by taking the variance difference of LBP features. Blocks with similar variance values are marked as forged pixels in the image. For decreasing the time complexity, edge detector is used which gives edge binary image for high gradient pixels in the image. First matching is carried out for edge pixel blocks only. In post processing, morphological operations are used and matching procedure is followed to get the forged pixels in the image. Experiment results are carried out on a standard dataset in which detection accuracy (DA) and false positive rate (FPR) are used for performance evaluation.

forgery detection, Rotation invariant, LBP, Zernike moments etc

[1] I. Amerini, L. Ballan, R.Caldelli, A. D. Bimbo, L.D. Tongo, G.Serra, “Copy-move forgery detection and localization by means of robust clustering with J-Linkage”, Signal Processing: Image Communication, Vol. 28, Issue. 6, pp.659-669, 2013
[2] R. Dixit, R. Naskar and A. Sahoo, "Copy-move forgery detection exploiting statistical image features," International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, pp. 2277-2281, 2017
[3] D. Cozzolino , G. Poggi , L. Verdoliva, “Efficient dense-field copy–move forgery detection”, IEEE Transactions on Information Forensics and Security, vol. 10, issue 11, pp. 2284-2297, 2015
[4] D. Tralic, I. Zupancic, S. Grgic, and M. Grgic, “CoMoFoD-New database for copy–move forgery detection,” 55th IEEE International Symposium ELMAR, 2013.
[5] N. Warif, A.Wahab, M. Idris, R.Ramli, R.Salleh, S.Shamshirband, K. Choo, "Copy-move forgery detection: Survey, challenges and future directions" Journal of Network and Computer Applications, Vol. 75, pp.259-278, 2016
[6] D. Chauhan, D. Kasat, S. Jain, V. Thakare, “Survey on Keypoint Based Copy-move Forgery Detection Methods on Image”, Procedia Computer Science, Vol. 85, pp. 206-212, 2016
[7] X. Bi, C. M. Pun “Fast reflective offset-guided searching method for copy-move forgery detection” Information Sciences, Vol. 418–419, pp. 531-545, 2017
[8] S. M. Fadl, N. A. Semary, “Robust Copy–Move forgery revealing in digital images using polar coordinate system”, Neuro computing, Vol. 265, pp. 57-65, 2017
[9] A. Kuznetsov, V. Myasnikov, “A new copy-move forgery detection algorithm using image preprocessing procedure”, Procedia Engineering, Vol. 201, pp. 436-444, 2017
[10] D. Vaishnavi, T.S. Subashini, “Application of local invariant symmetry features to detect and localize image copy move forgeries”, Journal of Information Security and Applications, Vol. 44, pp. 23-3, 2019
[11] Z. Xie, W. Lu, X. Liu, Y. Xue, Y. Yeung, “Copy-move detection of digital audio based on multi-feature decision”, Journal of Information Security and Applications, Vol. 43, pp. 37-46, 2018
[12] H. A. Alberry, A. A. Hegazy, G. I. Salama, “A fast SIFT based method for copy move forgery detection”, Future Computing and Informatics Journal, Vol. 3, Issue 2, pp. 159-165, 2018
[13] P. M. Raju, M. S. Nair, “Copy-move forgery detection using binary discriminant features”, Journal of King Saud University - Computer and Information Sciences, 2018
[14] J. Yang, P. Ran, D. Xiao, and J. Tan, “Digital image forgery forensics by using undecimated dyadic wavelet transform and Zernike moments”, Journal of Computational Information Systems, vol. 9,Issue 16, 2008.
[15] Suma S L, Sarika Raga, "Real Time Face Recognition of Human Faces by using LBPH and Viola Jones AlgorithmReal Time Face Recognition of Human Faces by using LBPH and Viola Jones Algorithm", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.5, pp.6-10, 2018
[16] Amey Samant, Sushma Kadge, "Classification of a Retinal Disease based on Different Supervised Learning Techniques", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.9-13, 2017