Region duplication detection based on image segmentation and keypoint contexts
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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.
KeywordsDigital image forensics Region duplication detection Copy-move forgery Image segmentation Keypoint contexts
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).
- 8.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, USAGoogle Scholar
- 9.Hartley R, Zisserman A (2004) Multiple view geometry in computer vision. Cambridge University PressGoogle Scholar
- 11.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 272–276Google Scholar
- 13.Khan S, Kulkarni A (2010) Reduced time complexity for detection of copy-move forgery using discrete wavelet transform. Int J Comput Appl 6(7):31–36Google Scholar
- 15.Lowe DG (2004) Distinctive image features from scale-invariant keypoints. In: International journal of computer vision, pp. 91–110Google Scholar
- 17.Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Tech. Rep. TR2004-515, Department of Computer Science, Dartmouth CollegeGoogle Scholar
- 20.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 51–65Google Scholar
- 21.Shivakumar BL, Baboo S (2011) Detection of region duplication forgery in digital images using SURF. Int J Comput Sci Issues 8(4):199–205Google Scholar
- 22.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 1469–1472Google Scholar