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Grey wolf assisted SIFT for improving copy move image forgery detection

  • Moka Uma DeviEmail author
  • Uppu Ravi Babu
Special Issue
  • 17 Downloads

Abstract

Copy-move forgery is a general widespread type of digital image forgery, where a segment of an image is attached into a new portion of the similar image to hide or replicate the parts which are called forgered image. The forgered image appears original, as the objective region in spite of being forged, has attained the fundamental qualities of the similar image itself. The capability of the copy-move forgery detection (CMFD) technique is lacked due to some post-processing functions, like JPEG compression scaling, or rotation, etc. Therefore, this paper intends to develop a CMFD using Scale-invariant feature transform (SIFT), best-fin-first algorithm (BBF) and RANdom SAmple Consensus (RANSAC) directed by grey wolf optimization (GWO) algorithm. Initially, the keypoints are selected using SIFT principle, and BBF algorithm identifies the matched keypoints using keypoint threshold. Further, SIFT feature descriptor is determined, and the final extracted paired keypoints are given to RANSAC algorithm to remove all the mismatched keypoints. In this CMFD model, the parameters such as parameters keypoint threshold, maximum distance of inliers in RANSAC and distance threshold in SIFT features are optimized using GWO. The foremost purpose of this research work is maximizing the number of paired keypoints. Hence the proposed model is termed as GWO-based parameter optimization for CMFD (GWPO-CMD). The proposed model is compared over several other meta-heuristic-based keypoint threshold selections and proves its efficiency through diverse analysis.

Keywords

Copy-move forgery detection SIFT features Keypoint threshold Best-bin-first GWO algorithm RANSAC algorithm 

Notes

References

  1. 1.
    Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518CrossRefGoogle Scholar
  2. 2.
    Fadl SM, Semary NA (2017) Robust copy–move forgery revealing in digital images using polar coordinate system. Neurocomputing 265:57–65CrossRefGoogle Scholar
  3. 3.
    Wenchang S, Fei Z, Bo Q, Bin L (2016) Improving image copy-move forgery detection with particle swarm optimization techniques. China Commun 13(1):139–149CrossRefGoogle Scholar
  4. 4.
    Lee J-C, Chang C-P, Chen W-K (2015) Detection of copy–move image forgery using histogram of orientated gradients. Inf Sci 321:250–262CrossRefGoogle Scholar
  5. 5.
    Dixit R, Naskar R, Mishra S (2017) Blur-invariant copy-move forgery detection technique with improved detection accuracy utilising SWT-SVD. IET Image Process 11(5):301–309CrossRefGoogle Scholar
  6. 6.
    Lee J-C (2015) Copy-move image forgery detection based on Gabor magnitude. J Vis Commun Image Represent 31:320–334CrossRefGoogle Scholar
  7. 7.
    Pun CM, Yuan XC, Bi XL (2015) Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Trans Inf Forensics Secur 10(8):1705–1716CrossRefGoogle Scholar
  8. 8.
    Mahmood T, Irtaza A, Mehmood Z, Mahmood MT (2017) Copy–move forgery detection through stationary wavelets and local binary pattern variance for forensic analysis in digital images. Forensic Sci Int 279:8–21CrossRefGoogle Scholar
  9. 9.
    Yang F, Li J, Wei L, Weng J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intell 59:73–83CrossRefGoogle Scholar
  10. 10.
    Warif NBA, Wahab AWA, Idris MYI, Ramli R, Choo KKR (2016) Copy-move forgery detection: Survey, challenges and future directions. J Netw Comput Appl 75:259–278CrossRefGoogle Scholar
  11. 11.
    Chauhan D, Kasat D, Jain S, Thakare V (2016) Survey on keypoint based copy-move forgery detection methods on image. Procedia Comput Sci 85:206–212CrossRefGoogle Scholar
  12. 12.
    Ustubioglu B, Ulutas G, Ulutas M, Nabiyev VV (2016) A new copy move forgery detection technique with automatic threshold determination. AEU Int J Electron Commun 70(8):1076–1087CrossRefGoogle Scholar
  13. 13.
    Lynch G, Shih FY, Liao HYM (2013) An efficient expanding block algorithm for image copy-move forgery detection. Inf Sci 239:253–265CrossRefGoogle Scholar
  14. 14.
    Davarzani R, Yaghmaie K, Mozaffari S, Tapak M (2013) Copy-move forgery detection using multiresolution local binary patterns. Forensic Sci Int 231(1–3):61–72CrossRefGoogle Scholar
  15. 15.
    Warif NBA, Wahab AWA, Idris MYI, Salleh R, Othman F (2017) SIFT-symmetry: a robust detection method for copy-move forgery with reflection attack. J Vis Commun and Image Represent 46:219–232CrossRefGoogle Scholar
  16. 16.
    Al-Qershi OM, Khoo BE (2013) Passive detection of copy-move forgery in digital images: state-of-the-art. Forensic Sci Int 231(1–3):284–295CrossRefGoogle Scholar
  17. 17.
    Schetinger V, Iuliani M, Piva A, Oliveira MM (2017) Image forgery detection confronts image composition. Comput Graph 68:152–163CrossRefGoogle Scholar
  18. 18.
    Warbhe AD, Dharaskar RV, Thakare VM (2016) a survey on keypoint based copy-paste forgery detection techniques. Procedia Comput Sci 78:61–67CrossRefGoogle Scholar
  19. 19.
    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):178–184CrossRefGoogle Scholar
  20. 20.
    Mahdian B, Saic S (2007) Detection of copy–move forgery using a method based on blur moment invariants. Forensic Sci Int 171(2–3):180–189CrossRefGoogle Scholar
  21. 21.
    Li L, Li S, Zhu H, Xiaoyue W (2014) Detecting copy-move forgery under affine transforms for image forensics. Comput Electr Eng 40(6):1951–1962CrossRefGoogle Scholar
  22. 22.
    Zhao J, Guo J (2013) Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Sci Int 233(1–3):158–166CrossRefGoogle Scholar
  23. 23.
    Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Dig Investig 10(3):226–245CrossRefGoogle Scholar
  24. 24.
    Li C, Ma Q, Xiao L, Li M, Zhang A (2017) Image splicing detection based on Markov features in QDCT domain. Neuro Comput 228:29–36Google Scholar
  25. 25.
    Li J, Wang Y, Wang Y (2012) Visual tracking and learning using speeded up robust features. Pattern Recogn Lett 33(16):2094–2101CrossRefGoogle Scholar
  26. 26.
    Azeem A, Sharif M, Shah JH, Raza M (2015) Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction. J Appl Res Technol 13(3):402–408CrossRefGoogle Scholar
  27. 27.
    Peng J, Peng S, Hu Y (2012) Partial least squares and random sample consensus in outlier detection. Anal Chim Acta 719:24–29CrossRefGoogle Scholar
  28. 28.
    Zhang X, Li H, Himed B (2017) Multistatic passive detection with parametric modelling of the IO waveform. Sig Process 141:187–198CrossRefGoogle Scholar
  29. 29.
    Edenborn HM, Howard BH, Sams JI, Vesper DJ, Edenborn SL (2017) Passive detection of Pb in water using rock phosphate agarose beads. J Hazardous Mater 336:240–248CrossRefGoogle Scholar
  30. 30.
    Sitara K, Mehtre BM (2016) Digital video tampering detection: An overview of passive techniques. Digit Investig 18:8–22CrossRefGoogle Scholar
  31. 31.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  32. 32.
    Zhu Y, Huang X, Huang Q, Tian Q (2016) Large-scale video copy retrieval with temporal-concentration SIFT. Neurocomputing 187:83–91CrossRefGoogle Scholar
  33. 33.
    Hossein-Nejad Z, Nasri M (2017) An adaptive image registration method based on SIFT features and RANSAC transform. Comput Electr Eng 62:524–537CrossRefGoogle Scholar
  34. 34.
    Vrionis TD, Koutiva XI, Vovos NA (2014) A genetic algorithm-based low voltage ride-through control strategy for grid connected doubly fed induction wind generators. IEEE Trans Power Syst 29:3CrossRefGoogle Scholar
  35. 35.
    Koçer B (2016) Bollinger bands approach on boosting ABC algorithm and its variants. Appl Soft Comput 49:292–312CrossRefGoogle Scholar
  36. 36.
    Zhang J, Xia P (2017) An improved PSO algorithm for parameter identification of nonlinear dynamic hysteretic models. J Sound Vib 389:153–167CrossRefGoogle Scholar
  37. 37.
    Fister I, Fister I, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46CrossRefGoogle Scholar
  38. 38.
    Lowe DG (1999) Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE international conference on in computer vision, vol 2, pp 1150–1157Google Scholar
  39. 39.
    Shankar A, Jaisankar N (2016) A novel energy efficient clustering mechanism in wireless sensor. Network 89:134–141Google Scholar
  40. 40.
    Sherifi I, Senja E (2018) Internet usage on mobile devices and their impact on evolution of informative websites. Albania 3(6):37–43Google Scholar
  41. 41.
    Kumar SBV, Rao PV, Sharath HA, Sachin BM, Ravi US, Monica BV (2018) Review on VLSI design using optimization and self-adaptive particle swarm optimization. J King Saud Univ Comput Inf Sci.  https://doi.org/10.1016/j.jksuci.2018.01.001 CrossRefGoogle Scholar
  42. 42.
    Thomas R, Rangachar MJS (2019) Hybrid optimization based DBN for face recognition using low-resolution images. Multimed Res 1(1):1–11Google Scholar
  43. 43.
    Shareef SKM, Rao RS (2018) Optimal reactive power dispatch under unbalanced conditions using hybrid swarm intelligence. Comput Electr Eng 69:183–193CrossRefGoogle Scholar
  44. 44.
    Barbari M, Monti M, Rossi G, Simonini S, Guerri FS (2014) Simple methods and tools to determine the mechanical strength of adobe in rural areas. J Food Agric Environ 12(2):904–909Google Scholar
  45. 45.
    Di Lecce V et al. (1999) Selection of reference signatures for automatic signature verification. In: Proceedings of the fifth international conference on document analysis and recognition. ICDAR ‘99 (Cat. No.PR00318), Bangalore, India, pp 597–600Google Scholar
  46. 46.
    Hooda M, Awasthi YK, Thakur N, Siddiqui AS (2019) A hybrid CS-CSA optimization algorithm for solving optimal power flow in single objective. Optimization 2(2):31–39Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Acharya Nagarjuna UniversityGunturIndia
  2. 2.D.R.K. Eng College and TechnologyHyderabadIndia

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