Abstract
The proposed scheme detects the copy-move forgery detection regions through the invariant features extracted from each block. First, an image is divided into overlapping blocks, and seven invariant moments of the maximum circle area in each block are calculated as moment features. Two clustering features, denoted by mean and variance of these seven moment features, are acquired for block comparison to reduce computation time. Therefore, the proposed scheme takes limited computation time because the seven moment features in each block are only compared to other blocks under the intersection of closed mean and variance features. The copy-move forgery regions can be found by matching the detected blocks with relative distance calculation. Experimental results show that the adopted moment features are efficient for detecting rotational or flipped duplicated regions.
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References
Al-Qershi OM, Khoo BE (2013) Passive detection of copy-move forgery in digital images: state-of-the-art. Forensic Sci Int 231:284–295
Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A SIFT-based forensic method for copy–move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6:1099–1110
Amerini I, Ballan L, Caldelli R, Del Bimbo A, Tongo LD, Serra G (2013) Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Process Image Commun 28:659–669
Bi X, Pun C, Yuan X (2016) Multi-level dense descriptor and hierarchical feature matching for copy-move forgery detection. Inf Sci 345:226–242
Bravo-Solorio S, Nandi AK (2011) Automated detection and localisation of duplicated regions affected by reflection, rotation and scaling in image forensics. Signal Process 91:1759–1770
Cao Y, Gao T, Fan L, Yang Q (2012) A robust detection algorithm for copy-move forgery in digital image. Forensic Sci Int 214:33–43
Chen CC, Tsai YH, Yeh HC (2016) Difference-expansion based reversible and visible image watermarking scheme. Multimed Tools Appl. doi:10.1007/s11042-016-3452-9
Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensics Secur 7:1841–1854
Cox I, Miller M, Bloom J, Fridrich J, Kalker T (2007) Digital watermarking and steganography, 2nd edn. Morgan Kaufmann, San Francisco
Davarzani R, Yaghmaie K, Mozaffari S, Tapak M (2013) Copy-move forgery detection using multiresolution local binary patterns. Forensic Sci Int 231:61–72
Farid H (2009) A survey of image forgery detection. IEEE Signal Process Mag 2:16–25
Fridrich J, Soukal D, Lukás J (2003) Detection of copy move forgery in digital images. In Proc. of conf. on digital forensic research workshop 55–61
Hsu CC, Hung TY, Lin CW, Hsu CT (2007) Video forgery detection using correlation of noise residue. In Proc. of IEEE int. conf. on multimedia signal processing 170–174
Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8(2):179–187
Kobayashi M, Okabe T, Sato Y (2010) Detecting forgery from static-scene video based on inconsistency in noise level functions. IEEE Trans Inf Forensics Secur 5(4):883–892
Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224(1–3):59–67
Li J, Li XL, Yang B, Sun XM (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10:507–518
Liao SY, Huang TQ (2013) Video copy-move forgery detection and localization based on tamura texture features. In Proc. of IEEE int. conf. on image and signal processing 864–868
Lin C, Tsay J (2014) A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis. Digit Investig 11(2):120–140
Lin CS, Chen CC, Chang YC (2015) An efficiency enhanced cluster expanding block algorithm for copy-move forgery detection. IEEE International Conference on Intelligent Networking and Collaborative Systems (INCOS)
Lynch G, Shih FY, Liao HM (2013) An efficient expanding block algorithm for image copy-move forgery detection. Inf Sci 239:253–265
Muhammad G, Hussain M, Bebis G Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit Investig 9:49–57
Popescu AC, Huang HF (2004) Exposing Digital Forgeries bydetecting duplicated image regions. Department of Computer Science TR2004–515
Ryu SJ, Kirchner M, Lee MJ, Lee HK (2013) Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans Inf Forensics Secur 8:1355–1370
Subramanyam AV, Emmanuel S (2013) Pixel estimation based video forgery detection. In Proc. of IEEE int. conf. on acoustics, speech and signal processing 3038–3042
Tralic D, Zupansis I, Grgic S, Grgic M (2013) CoMoFoD-New Database for copy-move forgery detection. The 55th internationsl symposium on ELMAR 49–54
Tu, HK, Thuong LT, Synh HVU, Khoa HV (2015) The efficiency of applying DWT and feature extraction into copy-move images detection. IEEE international conference on Advanced Technologies for Communications (ATC)
Zhao J, Guo J (2013) Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Sci Int 233:158–166
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Chen, CC., Wang, H. & Lin, CS. An efficiency enhanced cluster expanding block algorithm for copy-move forgery detection. Multimed Tools Appl 76, 26503–26522 (2017). https://doi.org/10.1007/s11042-016-4179-3
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DOI: https://doi.org/10.1007/s11042-016-4179-3