Improving the Computational Cost for Copied Region Detection in Forensic Images
This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.
 T. Huynh, T. Le, K. Huynh, and S. Nguyen, “A survey on image forgery detection techniques,” in The 11th IEEERIVF International Conference on Computing and Communication Technologies, 25-28 Jan 2015.
 W. Luo, J. Huang, and G. Qiu, “Robust detection of regionduplication forgery in digital image,” in The 18th IEEE International Conference on Pattern Recognition, 2006.
 B. Kang and M. We, “Identifying tampered regions using singular value decomposition in digital image forensics,” in The 2008 IEEE International Conference on Computer Science and Software Engineering, 12-14 Dec.
 C. Popescu and H. Farid, “Exposing digital forgeries by detecting duplicated image regions,” Dartmouth Computer Science Technical Report TR2004-515, USA, Tech. Rep., 2004.
 J. Lin, W. Wang, and T. Kao, “Fast copy-move forgery detection,” WSEAS Transactions on Signal Processing, vol. 5, pp. 188–197, 2009.
 C. Nguyen and S. Katzenbeisser, “Detection of copy-move forgery in digital images using radon transformation and phase correlation,” in The 8th IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 18-20 July.
 L. Li, S. Li, and H. Zhu, “An efficient scheme for detecting copy-move forged images by local binary patterns,” Journal of Information Hiding and Multimedia Signal Processing, vol. 4, pp. 46–56, 2013.
 H. Huang, W. Guo, and Z. Y., “Detection of copy-move forgery in digital images using radon transformation and phase correlation,” in The 8th IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 19-20 Dec.
 J. Ryu, J. Lee, and K. Lee, “Detection of copy-rotate-move forgery using zernike moments,” Information Hiding Lecture Notes in Computer Science, vol. 6387, pp. 51–65, 2010.
 J. Fridrich, D. Soukal, , and J. Luk, “Detection of copymove forgery in digital images,” in Digital Forensic Research Workshop, Cleveland, Ohio, USA, 2003.
 Y. Cao, T. Gao, L. Fan, and Q. Yang, “Detection of copy-rotatemove forgery using zernike moments,” Journal of Forensic Science Internationa, vol. 214, pp. 33–43, 2012.
 K. Bashar, K. Noda, N. Ohnishi, H. Kudo, T. Matsumoto, and Y. Takeuchi, “Wavelet-based multi-resolution features for detecting duplications in images,” in Conference on Machine Vision Applications, Tokyo, JAPAN, 2007.
 J. Yang, P. Ran, D. Xiao, and J. Tan, “Digital image forgery forensics by undecimated dyadic wavelet transform and zernike moments,” Journal of Computational Information Systems, vol. 9, pp. 6399–6408, 2013.
 R. Gonzalez and R. Woods, Digital Image Processing, 3rd ed. Prentice Hall.
 T. Le, T. Huynh, and T. Huynh, “The total error limits in duplicated image by modifying the paremeters of zernike moments computation,” in The 7th International Conference on Computer and Automation Engineering, 2015.
 V. Christlein, C. Riess, J. Jordan, C. Riess, and E. Angelopoulou, “An evaluation of popular copy-move forgery detection approaches,” IEEE Transactions on Information Forensics and Security, vol. 7, pp. 1841–1854, 2012.