Image Multiple Copy-move Forgery Detection Algorithm Based on Reversed-generalized 2 Nearest-neighbor
Li Yan① Liu Nian② Zhang Bin③ Yuan Kai-guo① Yang Yi-xian①
①(Information Security Center, Beijing University of Posts and Telecommunications, Beijing 100876, China) ②(Beijing Electronic Science and Technology Institute, Beijing 100070, China) ③(China Information Security Certification Center, Beijing 100020, China)
For the consideration of the multiple copy-move forgery detection of digital images, and to avoid missing the matching feature points when generalized 2 Nearest-Neighbor (g2NN) algorithm is applied, Reversed generalized 2 Nearest-Neighbor (Rg2NN) algorithm is proposed. Reverse order is used in feature points matching, so that all feature points that match with the detected point can be calculated accurately. The experiment results show that the matching feature points calculated by Rg2NN are more accurate than by g2NN, and the ability of g2NN in detecting multiple copy-move forgery is improved. When one patch in the image is copied and pasted multiple times or two or more patches are copied and pasted, the copy-move map can be localized precisely by the Rg2NN algorithm.
Qazi T, Hayat K, Khan S U, et al.. Survey on blind image forgery detection[J]. IET Image Processing, 2013, 7(7): 660-670.
[2]
Al-Qershi O M and Khoo B E. Passive detection of copy- move forgery in digital images: state-of-the-art[J]. Forensic Science International, 2013, 231(1/3): 284-295.
[3]
Ali Qureshi M and Deriche M. A review on copy move image forgery detection techniques[C]. Proceedings of the 11th International Multi-Conference on Systems, Signals & Devices (SSD), Barcelona, Spain, 2014: 1-5.
Wang Qing and Zhang Rong. Exposing digital image forgeries based on double quantization mapping relation of DCT coefficient[J]. Journal of Electronics & Information Technology, 2014, 36(9): 2068-2074.
[5]
Wu Y, Deng Y, Duan H, et al.. Dual tree complex wavelet transform approach to copy-rotate-move forgery detection[J]. SCIENCE CHINA Information Sciences, 2014, 57(1): 1-12.
[6]
Wang W, Dong J, and Tan T N. Exploring DCT coefficient quantization effects for local tampering detection[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(10): 1653-1666.
[7]
Niu S Z, Meng X Z, and Cui H L. Digital image forensics using orthogonal 1-D objects[J]. Chinese Journal of Electronics, 2014, 23(3): 545-549.
[8]
Liu B, Pun C M, and Yuan X C. Digital image forgery detection using JPEG features and local noise discrepancies [J]. The Scientific World Journal, 2014(1): 1-12.
[9]
Fridrich A J, Soukal B D, and Luká? A J. Detection of copy-move forgery in digital images[C]. Proceedings of the Digital Forensic Research Workshop, Cleveland, USA, 2003: 55-61.
[10]
Popescu A C and Farid H. Exposing digital forgeries by detecting duplicated image regions[R]. Department of Computer Science, Dartmouth College, 2004.
[11]
Jaberi M, Bebis G, Hussain M, et al.. Accurate and robust localization of duplicated region in copy-move image forgery [J]. Machine Vision and Applications, 2014, 25(2): 451-475.
[12]
Bo X, Junwen W, Guangjie L, et al.. Image copy-move forgery detection based on SURF[C]. Proceedings of the International Conference on Multimedia Information Networking and Security (MINES), Nanjing, China, 2010: 889-892.
[13]
Mishra P, Mishra N, Sharma S, et al.. Region duplication forgery detection technique based on SURF and HAC[J]. The Scientific World Journal, 2013(1): 1-8.
[14]
Chen L, Lu W, Ni J, et al.. Region duplication detection based on Harris corner points and step sector statistics[J]. Journal of Visual Communication and Image Representation, 2013, 24(3): 244-254.
[15]
Amerini I, Ballan L, Caldelli R, et al.. A SIFT-based forensic method for copy-move attack detection and transformation recovery[J]. IEEE Transactions on Information Forensics and Security, 2011, 6(3): 1099-1110.
[16]
Bay H, Ess A, Tuytelaars T, et al.. Speeded-up robust features (SURF)[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.
[17]
Beis J S and Lowe D G. Shape indexing using approximate nearest-neighbour search in high-dimensional spaces[C]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, 1997: 1000-1006.
[18]
Amerini I, Ballan L, Caldelli R, et al.. Copy-move forgery detection and localization by means of robust clustering with J-linkage[J]. Signal Processing: Image Communication, 2013, 28(6): 659-669.
[19]
Kakar P and Sudha N. Exposing postprocessed copy-paste forgeries through transform-invariant features[J]. IEEE Transactions on Information Forensics and Security, 2012, 7(3): 1018-1028.
[20]
Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[21]
Toldo R and Fusiello A. Robust multiple structures estimation with J-linkage[C]. Proceedings of the 10th European Conference on Computer Vision, Marseille, France, 2008, 5302: 537-547.
[22]
Suzuki S. Topological structural analysis of digitized binary images by border following[J]. Computer Vision, Graphics, and Image Processing, 1985, 30(1): 32-46.
[23]
Jegou H, Douze M, and Schmid C. Hamming embedding and weak geometric consistency for large scale image search[C]. Proceedings of the 10th European Conference on Computer Vision, Marseille, France, 2008, 5302: 304-317.
