Multimedia Tools and Applications

, Volume 77, Issue 20, pp 26939–26963 | Cite as

An integrated method of copy-move and splicing for image forgery detection

  • Choudhary Shyam PrakashEmail author
  • Avinash Kumar
  • Sushila Maheshkar
  • Vikas Maheshkar


Splicing and copy-move are two well known methods of passive image forgery. In this paper, splicing and copy-move forgery detection are performed simultaneously on the same database CASIA v1.0 and CASIA v2.0. Initially, a suspicious image is taken and features are extracted through BDCT and enhanced threshold method. The proposed technique decides whether the given image is manipulated or not. If it is manipulated then support vector machine (SVM) classify that the given image is gone through splicing forgery or copy-move forgery. For copy-move detection, ZM-polar (Zernike Moment) is used to locate the duplicated regions in image. Experimental results depict the performance of the proposed method.


Image forensics Copy-move forgery Duplicate region detection Image splicing BDCT Zernike moment 


  1. 1.
    Amerini I, Ballan L, Caldelli R, Bimbo AD, Serra G (2011) A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6(3):1099–1110CrossRefGoogle Scholar
  2. 2.
    Amerini I, Ballan L, Caldelli R, Bimbo AD, Tongo LD, Serra G (2013) Copy-move forgery detection and localization by means of robust clustering with j-linkage. Signal Process Image Commun 28(6):659–669CrossRefGoogle Scholar
  3. 3.
    Andreopoulos A, Tsotsos JK (2013) 50 years of object recognition directions forward. Comput Vis Image Underst 117(8):827–891CrossRefGoogle Scholar
  4. 4.
    Barnes C, Shechtman E, Finkelstein A, Goldman DB (2009) Patchmatch: A randomized correspondence algorithm for structural image editing. ACM Trans Graph 28(3):24–1CrossRefGoogle Scholar
  5. 5.
    Bayram S, Sencar HT, Memon N (2009) An efficient and robust method for detecting copy-move forgery. In: Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, pp 1053–1056. IEEEGoogle Scholar
  6. 6.
    Bianchi T, Piva A (2012) Image forgery localization via block-grained analysis of jpeg artifacts. IEEE Trans Inf Forensics Secur 7(3):1003–1017CrossRefGoogle Scholar
  7. 7.
    Bo S, Yuan Q, Wang S, Zhao C, Li S (2014) Enhanced state selection markov model for image splicing detection. EURASIP J Wirel Commun Netw 2014(1):7CrossRefGoogle Scholar
  8. 8.
    Bravo-Solorio S, Nandi AK (2011) Automated detection and localisation of duplicated regions affected by reflection, rotation and scaling in image forensics. Signal Process 91(8):1759–1770zbMATHCrossRefGoogle Scholar
  9. 9.
    Campos FM, Correia L, Calado JMF (2015) Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches. J Intell Rob Syst 77(2):377–390CrossRefGoogle Scholar
  10. 10.
    Cao Y, Gao T, Li F, Yang Q (2012) A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 214(1):33–43CrossRefGoogle Scholar
  11. 11.
    Chen L, Wei L, Ni J, Sun W, Huang J (2013) Region duplication detection based on harris corner points and step sector statistics. J Vis Commun Image Represent 24(3):244–254CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Dollar P, Wojek C, Schiele B, Perona P (2012) Pedestrian detection: An evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34(4):743–761CrossRefGoogle Scholar
  14. 14.
    Dong J, Wang W, Tan T (2013) Casia image tampering detection evaluation database. In: Signal and information processing (ChinaSIP), 2013 IEEE China Summit & International Conference on, pp 422–426. IEEEGoogle Scholar
  15. 15.
    El-Alfy E-SM, Qureshi MA (2015) Combining spatial and dct based markov features for enhanced blind detection of image splicing. Pattern Anal Applic 18 (3):713–723MathSciNetCrossRefGoogle Scholar
  16. 16.
    Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395MathSciNetCrossRefGoogle Scholar
  17. 17.
    Fridrich AJ, Soukal BD, Lukáš AJ (2003) Detection of copy-move forgery in digital images. In Inproceedings of Digital Forensic Research Workshop. CiteseerGoogle Scholar
  18. 18.
    Gionis A, Indyk P, Motwani R et al. (1999) Similarity search in high dimensions via hashing. In: VLDB, vol. 99, pp 518–529Google Scholar
  19. 19.
    Guo J-M, Liu Y-F, Wu Z-J (2013) Duplication forgery detection using improved daisy descriptor. Expert Syst Appl 40(2):707–714CrossRefGoogle Scholar
  20. 20.
    Hakimi F (2015) Image-splicing forgery detection based on improved lbp and k-nearest neighbors algorithm. Electron Inf Plan, 3Google Scholar
  21. 21.
    He Z, Wei L, Sun W, Huang J (2012) Digital image splicing detection based on markov features in dct and dwt domain. Pattern Recogn 45(12):4292–4299CrossRefGoogle Scholar
  22. 22.
    Hu W-C, Dai J-S, Jian J-S (2015) Effective composite image detection method based on feature inconsistency of image components. Digital Signal Process 39:50–62CrossRefGoogle Scholar
  23. 23.
    Huang Y, Wei L, Sun W, Long D (2011) Improved dct-based detection of copy-move forgery in images. Forensic Sci Int 206(1):178–184CrossRefGoogle Scholar
  24. 24.
    Huang D-Y, Huang C-N, Hu W-C, Chou C-H (2017) Robustness of copy-move forgery detection under high jpeg compression artifacts. Multimedia Tools and Applications 76(1):1509–1530CrossRefGoogle Scholar
  25. 25.
    Jain AK, Ross AA, Nandakumar K (2011) Introduction. In: Introduction to Biometrics, pp 1–49. SpringerGoogle Scholar
  26. 26.
    Kakar P, Sudha N (2012) Exposing postprocessed copy–paste forgeries through transform-invariant features. IEEE Trans Inf Forensics Secur 7(3):1018–1028CrossRefGoogle Scholar
  27. 27.
    Kodovsky J, Fridrich J, Holub V (2012) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inf Forensics Secur 7(2):432–444CrossRefGoogle Scholar
  28. 28.
    Langille A, Gong M (2006) An efficient match-based duplication detection algorithm. In: Computer Robot Vision The 3rd Canadian Conference on, pp 64–64. IEEEGoogle Scholar
  29. 29.
    Lazebnik S, Schmid C, Ponce J (2005) A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell 27(8):1265–1278CrossRefGoogle Scholar
  30. 30.
    Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224(1):59–67CrossRefGoogle Scholar
  31. 31.
    Li X, Jing T, Li X (2010) Image splicing detection based on moment features and hilbert-huang transform. In: 2010 IEEE international conference on information theory and information security (ICITIS), pp 1127–1130. IEEEGoogle Scholar
  32. 32.
    Li L, Li S, Zhu H, Wu X (2014) Detecting copy-move forgery under affine transforms for image forensics. Comput Electr Eng 40(6):1951–1962CrossRefGoogle Scholar
  33. 33.
    Li L, Li C, Ye L, Jia Y, Rosenblum DS (2016) Recognizing complex activities by a probabilistic interval-based model. In: AAAI, vol. 30, pp 1266–1272Google Scholar
  34. 34.
    Liu S, Bai X (2012) Discriminative features for image classification and retrieval. Pattern Recogn Lett 33(6):744–751CrossRefGoogle Scholar
  35. 35.
    Lu Y, Ye W, Li L, Zhong J, Sun L, Ye L (2017) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimedia Tools and Applications 76(8):10701–10719CrossRefGoogle Scholar
  36. 36.
    Mahdian B, Saic S (2007) Detection of copy–move forgery using a method based on blur moment invariants. Forensic Sci Int 171(2):180–189CrossRefGoogle Scholar
  37. 37.
    Moreels P, Perona P (2007) Evaluation of features detectors and descriptors based on 3d objects. Int J Comput Vis 73(3):263–284CrossRefGoogle Scholar
  38. 38.
    Muhammad G, Hussain M, Bebis G (2012) Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit Investig 9(1):49–57CrossRefGoogle Scholar
  39. 39.
    Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP (1) 2(331-340):2Google Scholar
  40. 40.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867CrossRefGoogle Scholar
  41. 41.
    Pevny T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224CrossRefGoogle Scholar
  42. 42.
    Pun C-M, Bo L, Yuan X-C (2016) Multi-scale noise estimation for image splicing forgery detection. J Vis Commun Image Represent 38:195–206CrossRefGoogle Scholar
  43. 43.
    Qiu X, Li H, Luo W, Huang J (2014) A universal image forensic strategy based on steganalytic model. In: Proceedings of the 2nd ACM workshop on Information hiding and multimedia security, pages 165–170. ACMGoogle Scholar
  44. 44.
    Rahmani R, Goldman SA, Zhang H, Krettek J, Fritts JE (2005) Localized content based image retrieval. In: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, pp 227–236. ACMGoogle Scholar
  45. 45.
    Ryu S-J, Lee M-J, Lee H-K (2010) Detection of copy-rotate-move forgery using zernike moments. In: Information hiding, vol. 6387, pp 51–65. SpringerGoogle Scholar
  46. 46.
    Ryu S-J, Kirchner M, Lee M-J, Lee H-K (2013) Rotation invariant localization of duplicated image regions based on zernike moments. IEEE Trans Inf Forensics Secur 8(8):1355–1370CrossRefGoogle Scholar
  47. 47.
    Shi Y, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: Proceedings of the 9th workshop on Multimedia & security, pp 51–62. ACMGoogle Scholar
  48. 48.
    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
  49. 49.
    Sutthiwan P, Shi Y, Su W, Ng T-T (2010) Rake transform and Edge statistics for image forgery detection. In: Multimedia and Expo (ICME), 2010 17th IEEE International Conference on, pp 1463–1468. IEEEGoogle Scholar
  50. 50.
    Sutthiwan P, Shi Y-Q, Dong J, Tan T, Ng T-T (2010) New developments in color image tampering detection. In: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, pp 3064–3067. IEEEGoogle Scholar
  51. 51.
    Sutthiwan P, Shi Y, Zhao H, Ng T-T, Su W (2011) Markovian rake transform for digital image tampering detection. Transactions on data hiding and multimedia security VI 6:1–17Google Scholar
  52. 52.
    Wang W, Dong J, Tan T (2009) Effective image splicing detection based on image chroma. Image Processing (ICIP), 2009 16th IEEE International Conference on, pp 1257–1260. IEEEGoogle Scholar
  53. 53.
    Wang W, Dong J, Tan T (2010) Image tampering detection based on stationary distribution of markov chain. In: Image Processing (ICIP), 2010 17th IEEE International Conference on, pp 2101–2104. IEEEGoogle Scholar
  54. 54.
    Wu X, Fang Z (2011) Image splicing detection using illuminant color inconsistency. In: 2011 3rd international conference on multimedia information networking and security (MINES), pp 600–603. IEEEGoogle Scholar
  55. 55.
    Xin Y, Pawlak M, Liao S (2007) Accurate computation of zernike moments in polar coordinates. IEEE Trans Image Process 16(2):581–587MathSciNetCrossRefGoogle Scholar
  56. 56.
    Yap P-T, Jiang X, Kot AC (2010) Two-dimensional polar harmonic transforms for invariant image representation. IEEE Trans Pattern Anal Mach Intell 32(7):1259–1270CrossRefGoogle Scholar
  57. 57.
    Ye L, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: Recognizing complex activities from sensor data, In: IJCAI, pp 1617–1623Google Scholar
  58. 58.
    Ye L, Nie L, Li L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  59. 59.
    Ye L, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: Predicting your career path, In: AAAI, pp 201–207Google Scholar
  60. 60.
    Zhang Q, Wei L, Weng J (2016) Joint image splicing detection in dct and contourlet transform domain. J Vis Commun Image Represent 40:449–458CrossRefGoogle Scholar
  61. 61.
    Zhao J, Zhao W (2013) Passive forensics for region duplication image forgery based on harris feature points and local binary patterns. Math Probl Eng 2013:12Google Scholar
  62. 62.
    Zhao X, Li J, Li S, Wang S (2011) Detecting digital image splicing in chroma spaces. Digital Watermarking 6526:12–22CrossRefGoogle Scholar
  63. 63.
    Zhao X, Wang S, Li S, Li J (2015) Passive image-splicing detection by a 2-d noncausal markov model. IEEE Trans Circuits Syst Video Technol 25(2):185–199CrossRefGoogle Scholar
  64. 64.
    Zou D, Shi YQ, Su W, Xuan G (2006) Steganalysis based on markov model of thresholded prediction-error image. In: Multimedia and Expo, 2006 IEEE International Conference on, pp 1365–1368. IEEEGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (Indian School of Mines)DhanbadIndia

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