Local patterns in the copy-move detection problem solution
N.I. Evdokimova, A.V. Kuznetsov

 

Samara National Research University, Samara, Russia,
Image Processing Systems Institute оf RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Samara, Russia

Full text of article: Russian language.

 PDF

Abstract:
Embedding of duplicates is one of commonly used methods of image forgery. During this process, an image fragment is copied and pasted to another position in the same image. This is performed to conceal some important part of the image. A copy-move forgery detection algorithm aims to recognize duplicated areas in the image. This algorithm is based on calculating the characteristics in a sliding or overlapping window. In this paper, we compare the performance of copy-move detection algorithms that utilize a local binary pattern, a local ternary pattern, a local derivative pattern, and some extensions thereof. A distinctive feature of the used characteristics is their resistance to distortions inserted into the copy, such as linear contrast enhancement and impulse noise. This method also has low computational complexity.

Keywords:
copy-move, forgery, local binary pattern, local ternary pattern, local derivative pattern.

Citation:
Evdokimova NI, Kuznetsov AV. Local patterns in the copy-move detection problem solution. Computer Optics 2017; 41(1): 79-87. DOI: 10.18287/2412-6179-2017-41-1-79-87.

References:

  1. Christlein V, Riess C, Jordan J, Angelopoulou E. An Evaluation of Popular Copy-Move Forgery Detection Approaches. IEEE Transactions on information forensics and security 2012; 7(6): 1841-1854. DOI: 10.1109/TIFS.2012.2218597.
  2. Popescu A, Farid H. Exposing digital forgeries by detecting duplicated image regions. Source: áhttp://www.ists.dart­mouth.edu/library/102.pdfñ.
  3. Fridrich J, Soukal D, Lukáš J. Detection of copy-move forgery in digital images. Source: áhttp://www.ws.bing­hamton.edu/fridrich/Research/copymove.pdfñ.
  4. Wang L, He D-C. Texture classification using texture spectrum. Pattern Recognition 1990; 23(8): 905-910. DOI: 10.1016/0031-3203(90)90135-8.
  5. 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. DOI: 10.1109/TIP.2013.2268976.
  6. Heikkilä M, Pietikäinen M, Schmid C. Description of interest regions with local binary patterns. Pattern Recognition 2009; 42(3): 425-436. DOI: 10.1016/j.patcog.2008.08.014.
  7. Jin H, Liu Q, Lu H, Tong X. Face detection using improved LBP under bayesian framework. Proceedings of the 3rd International Conference on Image and Graphics 2004; 306-309. DOI: 10.1109/ICIG.2004.62.
  8. Hafiane A, Seetharaman G, Zavidovique B. Median binary pattern for textures classification. ICIAR '07 2007; 387-398. DOI: 10.1007/978-3-540-74260-9_35.
  9. Tan X, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing 2010; 19(6): 1635-1650. DOI: 10.1109/TIP.2010.2042645.
  10. Nanni L, Brahnam S, Lumini A. A local approach based on a Local Binary Patterns variant texture descriptor for classifying pain states. Expert Systems with Application 2010; 37(12): 7888-7894. DOI: 10.1016/j.eswa.2010.04.048.
  11. Zhang B, Gao Y, Zhao S, Liu J. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor.IEEE Transactions on Image Processing 2010; 19(2): 533-544. DOI: 10.1109/TIP.2009.2035882.
  12. Glumov NI, Kuznetsov AV, Myasnikov VV. The algorithm for copy-move detection on digital images [In Russian]. Computer optics 2013; 37(3): 360-367.
  13. Kuznetsov A, Myasnikov V. A Fast Plain Copy-Move Detection Algorithm Based on Structural Pattern and 2D Rabin-Karp Rolling Hash. In book: Campilho A, Kamel M, eds. Image Analysis and Recognition: 11th International Conference, ICIAR 2014, Vilamoura, Portugal, October 22-24, 2014, Proceedings, Part I 2014: 461-468. DOI: 10.1007/978-3-319-11758-4_50.

© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; E-mail: journal@computeroptics.ru; Phones: +7 (846) 332-56-22, Fax: +7 (846) 332-56-20