Abstract
In this paper, a novel region duplication detection method is proposed based on image segmentation and keypoint contexts. The proposed method includes the primary region duplication detection based on keypoints and the supplementary region duplication detection based on blocks. In the primary region duplication detection, an image is divided into non-overlapped patches by using SLIC. Furthermore, the keypoints are matched and clustered within the same patch as patch feature. Then the patches are matched and an affine transformation is tried to be estimated from a pair of patches. When the estimation fails, in the supplementary region duplication detection, a transformation matrix is tried to be estimated from a pair of keypoints by the proposed Keypoint Contexts (KC) approach. The experimental results indicate that the proposed method can achieve much better comprehensive performances than the state-of-the-art methods on the public databases, even under various challenging conditions.
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References
Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282
Amerini I, Ballan L, Caldelli R, Bimbo AD, Serra G (2011) A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6(3):1099–1110
Amerini I, Ballan L, Caldelli R, Bimbo AD, Tongo LD, Serra G (2013) Copy-move forgery detection and localization by means of robust clustering with J-Linkage. Signal Process Image Commun 28(6):659–669
Bay H, Ess A, Tuytelaars T, Gool LV (2008) SURF: speeded up robust features. Comput Vis Image Underst 110(3):346–359
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(6):1841–1854
Farid H (2009) Photo fakery and forensics. Adv Comput 77:1–55
Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395
Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. In: Proceeding of digital forensic research workshop, pp 19–23. Cleveland, OH, USA
Hartley R, Zisserman A (2004) Multiple view geometry in computer vision. Cambridge University Press
Hastie T, Tibshirani R, Friedman J (2003) The elements of statistical learning. Springer Series in Statistics, Berlin
Huang H, Guo W, Zhang Y (2008) Detection of copy-move forgery in digital images using SIFT algorithm. In: IEEE Pacific-Asia workshop on computational intelligence and industrial application, vol 2, pp 272–276
Huang Y, Lu W, Sun W, Long D (2011) Improved DCT-based detection of copy-move forgery in images. Forensic Sci Int 206(1-3):178–184
Khan S, Kulkarni A (2010) Reduced time complexity for detection of copy-move forgery using discrete wavelet transform. Int J Comput Appl 6(7):31–36
Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. In: International journal of computer vision, pp. 91–110
Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867
Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Tech. Rep. TR2004-515, Department of Computer Science, Dartmouth College
Pun CM, Yuan XC, Bi XL (2015) Image forgery detection using adaptive over-segmentation and feature points matching. IEEE Trans Inf Forensics Secur 10 (8):1705–1716
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(8):1355–1370
Ryu SJ, Lee MJ, Lee HK (2010) Detection of copy-rotate-move forgery using Zernike moments. In: IEEE International workshop on information hiding (IH). Springer, Berlin, pp 51–65
Shivakumar BL, Baboo S (2011) Detection of region duplication forgery in digital images using SURF. Int J Comput Sci Issues 8(4):199–205
Vedaldi A, Fulkerson B (2010) Vlfeat: an open and portable library of computer vision algorithms. In: International conference on multimedea 2010, Firenze, Italy, October, pp 1469–1472
Acknowledgments
This work is supported by the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Fundamental Research Funds for the Central Universities (No. 16lgjc83), Scientific and Technological Achievements Transformation Plan of Sun Yat-sen University, the Research Project of Guangdong University of Finance and Economics (No. 10GL52001), Shanghai Sailing Program (No. 17YF1420000), the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University (Contract No. RCS2016K007), and the Science and Technology Development Fund of Macao SAR (No. 097/2013/A3).
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Lin, C., Lu, W., Sun, W. et al. Region duplication detection based on image segmentation and keypoint contexts. Multimed Tools Appl 77, 14241–14258 (2018). https://doi.org/10.1007/s11042-017-5027-9
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DOI: https://doi.org/10.1007/s11042-017-5027-9