Now a day's images are tampered easily because availability of powerful image processing software and improvement of human computer knowledge. Manipulation of digital images in different fields like court of law and medical imaging create a serious problem nowadays. With rapid advances in digital image processing software, there is a widespread development of advanced tools and techniques for digital image forgery. The most common types of forgery is Copy-move forgery which copies some part of the image and pastes it to another part of the same image to cover an important scene. In this paper, the proposed method to detect Copy-Move forgery is by matching the mean and DCT low frequency coefficient components of each block with remaining all blocks. The color image is converted from RGB color space to YCbCr color space. Y-component is partitions into fixed-size overlapping blocks and, features are extracted from each image blocks. The feature vectors obtained are then lexicographically sorted to make similar image blocks neighbors and duplicated image blocks are identified using Euclidean distance as similarity criterion. The experimental results prove that the proposed method works on reasonable time and works well for gray scale and color images. In this method by using the comparison of mean value and sorting technique helps to reduced the computational complexity.

1. C. L. Jing, "Image copy-move forgery detecting based on local invariant feature," Journal of Multimedia, vol. 7, 2012.
2. Q. S. W. Chen and W. Su, "Image splicing detection using 2-d phase congruency and statistical moments of characteristic function," E. J. Delp and P. W. Wong, editors, Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505, p. 65050, 2007.
3. J. Fridrich, D. Soukalm, J. Lukáš, "Detection of copy-move forgery in digital images", In proceedings of the Digital Forensic Research Workshop, Cleveland, pp. 19–23, 2003.
4. Alin C. Popescu, H. Farid, "Exposing Digital Forgeries By Detecting Duplicated Image Regions", Technical Report TR2004-515, Dartmouth College, 2004.
5. Li Kang, Xiao-pin Cheng, "Copy-move forgery detection in digital image", 3rd International Congress on Image and Signal Processing (CISP), vol. 5, pp. 2419 – 2421, 2010.
6. Weiqi Luo, Jiwu Huang, Guoping Qiu, "Robust Detection of Region-Duplication Forgery in Digital Images", In proceedings of the International Conference on Pattern Recognition, Washington, DC, pp. 746-749, 2006.
7. Weihai Li and Nenghai Yu, "Rotation Robust Detection of Copy-move Forgery", In proceedings of the IEEE 17th nternational Conference on Image Processing, Hong Kong, 2010.
8. Yanping Huang, Wei Lu, Wei Sun and Dongyang Long, "Improved DCT-based Detection of Copy-Move Forgery in Images", Forensic Science International, vol. 206, pp. 178-184, 2011.
9. Yanjun Cao, Tiegang Gao, Li Fan, Qunting Yang, "A robust detection algorithm for copy-move forgery in digital images", Forensic Science International, vol. 214, pp. 33–43, 2012.
10. A. C. Popescu and H. Farid, "Exposing digital forgeries by detecting duplicated image regions," Dept. Computer. Science, Dartmouth College, Tech. Rep. TR2004-515, 2004.
11. Li G. , Wu, Q. , Tu, D. , Sun, S. : A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In: Proc. of ICME. (2007).
12. Luo W. , Huang, J. , Qiu, G. : Robust detection of region-duplication forgery in digital image. In: Proc. of ICPR. (2006).
13. N. Diane Wandji, S. Xingming, M. Fah Kue: "Detection of copy-move forgery in digital images based on DCT", IJCSI International Journal of Computer Science, Issues, Vol. 10, Issue 2, No 1, March 2013.

Copy Move Detection Image Forgery Discrete Cosine Transform Image Tempering Duplication Of Region.