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Abstract:
Copy-move is one of the most obvious ways of deliberate distortion of digital images in order to conceal the information contained in them. The process of duplicate embedding consists in copying an image fragment and pasting it within the same image. Prior to pasting, the fragment can be distorted using transformations such as contrast enhancement, noise adding, scaling, rotation, and combinations thereof. Existing approaches to copy-move forgery detection are based on calculating feature vectors for overlapping blocks of an image and then using these vectors to find the closest regions in Euclidean space. In this paper, we propose features based on binary gradient contours, which are resistant to contrast enhancement, additive noise and JPEG compression. We also present results of conducted experiments for demonstrating the proposed algorithm effectiveness for a range of distortion parameters. The research also involves comparing features based on binary gradient contours with features based on various forms of local binary patterns.

Keywords:
copy-move detection, distorted duplicate, local binary pattern, binary gradient contours, feature vector, k-d tree.

Citation:
Kuznetsov AV, Myasnikov VV. A copy-move detection algorithm based on binary gradient contours. Computer Optics 2016; 40(2): 284-93. DOI: 10.18287/2412-6179-2016-40-2-284-293.

References:

1. The Top 20 Valuable Facebook Statistics. Source: áhttps://zephoria.com/top-15-valuable-facebook-statisticsñ.
   
2. Christlein V, Riess C, Jordan J, Angelopoulou E. An Evaluation of Popular Copy-Move Forgery Detection Approaches. IEEE Trans-actions on information forensics and security 2012; 7(6): 1841-1854.
   
3. Glumov NI, Kuznetsov AV, Myasnikov VV. The algorithm for copy-move detection on digital images. Computer Optics 2013; 37(3): 360-367.
   
4. Kuznetsov AV, Myasnikov VV. Efficient linear local features based copy-move detection algorithm. Computer Optics 2013; 37(4): 489-495.
   
5. Kuznetsov AV, Myasnikov VV. A fast plain copy-move detection algorithm based on structural pattern and 2D Rabin-Karp rolling hash. LNCS 2014; 8814: 461-468.
   
6. Mahdian B, Saic S. Detection of Copy-Move Forgery using a Method Based on Blur Moment Invariants. Forensic Science Interna-tional 2007; 171(2): 180-189.
   
7. Ryu S, Lee M, Lee H. Detection of Copy-Rotate-Move Forgery using Zernike Moments. Information Hiding Conference 2010: 51-65.
   
8. Popescu A, Farid H. Exposing digital forgeries by detecting duplicated image regions. Source: áhttp://www.ists.dartmouth.edu/library/102.pdfñ.
   
9. Kang X, Wei S. Identifying Tampered Regions Using Singular Value Decomposition in Digital Image Forensics. International Con-ference on Computer Science and Software Engineering 2008; 3: 926-930.
   
10. Luo W, Huang J, Qiu G. Robust Detection of Region-Duplication Forgery in Digital Images. International Conference on Pattern Rec-ognition 2006; 4: 746-749.
    
11. Bravo-Solorio S, Nandi AK. Exposing Duplicated Regions Affected by Reflection, Rotation and Scaling. International Conference on Acoustics, Speech and Signal Processing 2011: 1880-1883.
    
12. Fridrich J, Soukal D, Lukas J. Detection of copy-move forgery in digital images. Source: áhttp://www.ws.binghamton.edu/fridrich/Research/copymove.pdfñ.
    
13. Bayram S, Sencar H, Memon H. An efficient and robust method for detecting copy-move forgery. IEEE International Conference on Acoustics, Speech, and Signal Processing 2009: 1053-1056.
    
14. Huang H, Guo W, Zhang Y. Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm. Pacific-Asia Workshop on Computational Intelligence and Industrial Application 2008; 2: 272-276.
    
15. Shivakumar BL, Baboo S. Detection of Region Duplication Forgery in Digital Images Using SURF. International Journal of Computer Science 2011; 8(4): 199-205.
    
16. Li L, Li S, Zhu H. An Efficient Scheme for Detection Copy-move Forged Images by Local Binary Patterns. Journal of Information Hiding and Multimedia Signal Processing 2013; 4(1): 46-56.
    
17. Davarzani R, Yaghmaie K, Mozaffari S, Tapak M. Copy-move forgery detection using multi-resolution local binary patterns. Forensic Science International 2013; 231(1-3): 61-72.
    
18. Ren J, Jiang X, Yuan J. Noise-Resistant Local Binary Pattern with an Embedded Error-Correction Mechanism. IEEE Transactions on Image Processing 2013; 22(10): 4049-4060.
    
19. Fernández A, Álvarez MX, Bianconi F. Image classification with binary gradient contours. Opt Lasers Eng 2011; 49(9-10): 1177-1184.
    
20. Wang L, He D-C. Texture classification using texture spectrum. Pattern Recognition 1990; 23(8): 905-910.
    
21. Ojala T, Pietikinen M, Harwood D. A comparative study of texture measures with classification based on featured distribution. Pattern Recognition 1996; 29(1): 51-59.
    
22. Ojala T, Pietikinen M, Menp T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002; 24(7): 971-987.
    
23. Myasnikov VV. A local order transform of digital images. Computer Optics 2015; 39(3): 397-405.
    
24. Arasteh S, Hung C-C. Color and texture image segmentation using uniform local binary patterns. Machine Graphics and Vision 2006; 15(3-4): 265-274.
    
25. Guo ZH, Zhang D. A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Pro-cessing 2010; 19(6): 1657-1663.
    
26. Zhao Y, Jia W, Hu RX, Min H. Completed robust local binary pattern for texture classification. Neuro-computing 2013; 106: 68-76.
27. Bentley JL. Multidimensional binary search trees used for associative searching. Communications of the ACM 1975; 18(9): 509-517.

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