A novel method for digital image copy-move forgery detection and localization using evolving cellular automata and local binary patterns

  • Gulnawaz Gani
  • Fasel QadirEmail author
Original Paper


Copy-Move Forgery Detection (CMFD) methods aim to forensically analyze a digital image for a possible content duplication manipulation. In the past, many block-based algorithms have been proposed for detection and localization of CMF. However, the existing solutions show limited efficacy for images compressed in JPEG and lack robustness against post-processing attacks such as noise addition, blurring, etc. To address this problem, we propose a new block-based passive method for detection and localization of CMF in this paper. Passive methods, as opposed to active methods, are used to authenticate the image content in the absence of any pre-embedded information such as watermarks. In our proposed scheme, a suspicious input image to be analyzed is first low pass filtered and converted to Local Binary Patterns (LBP) image. The LBP texture image is then divided into overlapping blocks. Next, a compact five-dimensional feature vector is extracted from each block by employing thresholding and Cellular Automata. The set of feature vectors is sorted lexicographically to bring the copy-pasted blocks nearer to each other. Finally, the feature matching step is used to reveal the duplicate blocks. Our experimental results indicate that the proposed method performs exceptionally well relative to other state-of-art-methods, under different image manipulation scenarios.


Copy-Move Forgery Cellular Automata Passive method Thresholding Local Binary Patterns 



  1. Al-Qershi OM, Khoo BE (2018) Evaluation of copy-move forgery detection: datasets and evaluation metrics. Multimed. Tools Appl. 77:31807–31833. CrossRefGoogle Scholar
  2. Amerini I, Ballan L, Member S, Caldelli R, Bimbo A Del, Serra G (2011) A SIFT-based forensic method for copy—move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6:1099–1110CrossRefGoogle Scholar
  3. Angelov P, Sadeghi-tehran P, Ramezani R (2011) An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi—Sugeno fuzzy systems. Int J Intell Syst 26:189–205. CrossRefzbMATHGoogle Scholar
  4. Ardizzone E, Bruno A, Mazzola G (2015) Copy-move forgery detection by matching triangles of keypoints. IEEE Trans Inf Forensics Secur 10(10):2084–2094. CrossRefGoogle Scholar
  5. Billings SA, Yang Y (2003) Identification of the neighborhood and CA rules from spatio-temporal CA patterns. IEEE Trans Syst Man Cybern Part B Cybern 33:332–339. CrossRefGoogle Scholar
  6. Cao Y, Gao T, Fan L, Yang Q (2012) A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 214:33–43. CrossRefGoogle Scholar
  7. 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):1841–1854CrossRefGoogle Scholar
  8. Davarzani R, Yaghmaie K, Mozaffari S, Tapak M (2013) Copy-move forgery detection using multiresolution local binary patterns. Forensic Sci Int 231:61–72. CrossRefGoogle Scholar
  9. Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. Int J Comput Sci Issues. CrossRefGoogle Scholar
  10. Hayat K, Qazi T (2017) Forgery detection in digital images via discrete wavelet and discrete cosine transforms. Comput Electr Eng. CrossRefGoogle Scholar
  11. Huang Y, Lu W, Sun W, Long D (2011) Improved DCT-based detection of copy-move forgery in images. Forensic Sci Int 206:178–184. CrossRefGoogle Scholar
  12. Jeelani Z, Qadir F (2018) Cellular automata-based approach for digital image scrambling. Int J Intell Comput Cybern 11:353–370CrossRefGoogle Scholar
  13. Jeelani Z, Qadir F (2019) Cellular automata-based approach for salt-and-pepper noise filtration. J King Saud Univ Comput Inf Sci. CrossRefGoogle Scholar
  14. Krawetz N (2015) Digital photo forensics, handbook of digital. Imaging. CrossRefGoogle Scholar
  15. Lee JC, Chang CP, Chen WK (2015) Detection of copy-move image forgery using histogram of orientated gradients. Inf Sci (NY) 321:250–262. CrossRefGoogle Scholar
  16. Li L, Li S, Zhu H, Chu S-C, Roddick JF, Pan J-S (2013) An efficient scheme for detecting copy-move forged images by local binary patterns. J Inf Hiding Multimed Signal Process 4:46–56Google Scholar
  17. Lin X, Li JH, Wang SL, Liew AWC, Cheng F, Huang XS (2018) Recent advances in passive digital image security forensics: a brief review. Engineering. CrossRefGoogle Scholar
  18. Mahmood T, Mehmood Z, Shah M, Saba T (2018) A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform. J Vis Commun Image Represent 53:202–214. CrossRefGoogle Scholar
  19. Mehta R, Egiazarian K (2016) Dominant rotated local binary patterns (DRLBP) for texture classification. Pattern Recognit Lett 71:16–22. CrossRefGoogle Scholar
  20. Nightingale SJ, Wade KA, Watson DG (2017) Can people identify original and manipulated photos of real-world scenes? Cogn Res Princ Implic 2:30. CrossRefGoogle Scholar
  21. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66. CrossRefGoogle Scholar
  22. Pun CM, Chung JL (2018) A two-stage localization for copy-move forgery detection. Inf Sci (NY) 463–464:33–55. MathSciNetCrossRefGoogle Scholar
  23. Qadir F, Shoosha IQ (2018) Cellular automata-based efficient method for the removal of high-density impulsive noise from digital images. Int J Inf Technol 10:529–536. CrossRefGoogle Scholar
  24. Qadir F, Peer MA, Khan KA (2013) Digital image scrambling based on two dimensional cellular automata. Int J Comput Netw Inf Secur 5:36–41. CrossRefGoogle Scholar
  25. Rosin PL (2010) Image processing using 3-state cellular automata. Comput Vis Image Underst. CrossRefGoogle Scholar
  26. 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. CrossRefGoogle Scholar
  27. Shelke PM, Prasad RS (2016) Improving JPEG image anti-forensics. 1:1.
  28. Sun X, Rosin PL, Martin RR (2011) Fast rule identification and neighborhood selection for cellular automata. IEEE Trans Syst Man Cybern Part B Cybern. CrossRefGoogle Scholar
  29. Tralic D, Zupancic I, Grgic M (2013) New database for copy-move forgery detection-CoMoFoD. In: 55th International Symposium ELMAR, pp 49–54Google Scholar
  30. Tralic D, Grgic S, Sun X, Rosin PL (2016) Combining cellular automata and local binary patterns for copy-move forgery detection. Multimed Tools Appl 75:16881–16903. CrossRefzbMATHGoogle Scholar
  31. Wang H, Wang H (2018) Perceptual hashing-based image copy-move forgery detection. Secur Commun Netw 2018:6853696. CrossRefGoogle Scholar
  32. Wen B, Zhu Y, Subramanian R, Ng TT, Shen X, Winkler S (2016) COVERAGE—a novel database for copy-move forgery detection. In: Proceedings—international conference on image processing. ICIP, pp 161–165.
  33. Wolfram S (2002) Stephen Wolfram: a new kind of science. [WWW Document]. Wolfram MediaGoogle Scholar
  34. Xu B, Wang J, Liu G, Dai Y (2010) Image copy-move forgery detection based on SURF. In: Proc.—2010 2nd Int. Conf. Multimed. Inf. Netw. Secur. MINES 2010, pp 889–892.
  35. 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. CrossRefGoogle Scholar
  36. Zhou X, Angelov P (2007) Autonomous visual self-localization in completely unknown environment using evolving fuzzy rule-based classifier. In: Proceedings of the 2007 IEEE symposium on computational intelligence in security and defense applications (CISDA 2007). IEEE, Honolulu, pp 131–138.

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of KashmirBaramullaIndia

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