Today manipulation of digital images has become easy due to powerful computers, advanced photo-editing software packages and high resolution capturing devices. Verifying the integrity of photo without any kind of special watermark or any prior knowledge is a critical issue. Photograph tampering techniques like copy-paste, which is very easy and effective to use, can extend image forging. The original content of the picture is copied to the desired locations. The increasing image modification software can easily manipulate the digital photo without leaving any visible clue. It’s important to study these issues because tempered photographs can cause social chaos, criminal and non-public consequences. It’s very important and additionally tough to discover the digital photograph forgeries. The main focus of this paper is to detect picture replica circulate forgery which is depended on SIFT (scale invariant feature transform) descriptors, which are invariant to rotation, scaling etc. Clustering algorithm is used for clustering of key points in images. Results show that, in comparison of existing methods MROGH, SURF-PHA provides consistent precision, recall and F1 score about 98.86%, 99.40%, and 99.13% respectively for the provided dataset. Experimental results indicate that this method is a robust method in detecting the copy-move forgery quickly and withstands certain transformations.

Copy-move Image Forgery, Forgery Detection, Feature Extraction, key-points, SIFT, Clustering

[1] https://www.researchgate.net/figure/Example-of-copy-move-forgery-a-Original-image-b-Forged-image-duplicated-object-h_fig1_317495890.
[2] V. Christlein, C. Riess, J. Jordan, C. Riess, and E. Angelopoulou, “An evaluation of popular copy-move forgery detection approaches,” Information Forensics and Security, IEEE Transactions on, vol. 7, no. 6, pp. 1841–1854, 2012.
[3] O. M. Alqershi, B. E. Khoo, “Passive detection of copy-move forgery in digital images: State-of-the-art,” Forensic Science International, vol. 231, no. 1, pp. 284–95, 2013.
[4] M. Emam, Q. Han, and X. Niu, “PCET based copy-move forgery detection in images under geometric transforms,” Multimedia Tools and Applications, vol. 75, no. 18, pp. 11513-11527, 2016.
[5] J. C. Lee, “Copy-move image forgery detection based on Gabor magnitude,” Journal of Visual Communication and Image Representation, vol. 31, pp. 320-334, 2015.
[6] I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, and G. Serra, “A siftbased forensic method for copy–move attack detection and transformation recovery,” Information Forensics and Security, IEEE Transactions on, vol. 6, no. 3, pp. 1099–1110, 2011.
[7] B. Shivakumar and L. D. S. S. Baboo, “Detection of region duplication forgery in digital images using surf,” IJCSI International Journal of Computer Science Issues, vol. 8, no. 4, pp. 199-205, 2011.
[8] H. Huang, W. Guo, and Y. Zhang, “Detection of copy-move forgery in digital images using sift algorithm,” in Computational Intelligence and Industrial Application, 2008. PACIIA’08. Pacific-Asia Workshop on, vol. 2. IEEE, 2008, pp. 272–276.
[9] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision, vol. 60, no. 2, pp. 91–110, 2004.
[10] X. Pan and S. Lyu, “Detecting image region duplication using sift features,” in Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on. IEEE, 2010, pp. 1706–1709.
[11] Emam , Mahmoud, et al. "A robust detection algorithm for image Copy-Move forgery in smooth regions." Circuits, System and Simulation (ICCSS), 2017 International Conference on. IEEE , 2017.
[12] Yadav, Preeti, and Yogesh Rathore. "Detection of copy-move forgery of images using discrete wavelet transform." International Journal on Computer Science and Engineering4.4 (2012): 565.
[13] Jaberi, Maryam, et al. "Improving the detection and localization of duplicated regions in copy-move image forgery." Digital Signal Processing (DSP), 2013 18th International Conference on. IEEE, 2013.
[14] Y. Lu, Y. Wan, Clustering by sorting potential values (CSPV): A novel potential-based clustering method, Pattern Recognition, 45(9) (2012), pp. 3512–3522.