Multimedia Tools and Applications

, Volume 78, Issue 21, pp 30081–30096 | Cite as

Copy-move forgery detection using combined features and transitive matching

  • Cong Lin
  • Wei LuEmail author
  • Xinchao Huang
  • Ke Liu
  • Wei Sun
  • Hanhui Lin
  • Zhiyuan Tan


Recently, the research of Internet of Things (IoT) and Multimedia Big Data (MBD) has been growing tremendously. Both IoT and MBD have a lot of multimedia data, which can be tampered easily. Therefore, the research of multimedia forensics is necessary. Copy-move is an important branch of multimedia forensics. In this paper, a novel copy-move forgery detection scheme using combined features and transitive matching is proposed. First, SIFT and LIOP are extracted as combined features from the input image. Second, transitive matching is used to improve the matching relationship. Third, a filtering approach using image segmentation is proposed to filter out false matches. Fourth, affine transformations are estimated between these image patches. Finally, duplicated regions are located based on those affine transformations. The experimental results demonstrate that the proposed scheme can achieve much better detection results on the public database under various attacks.


Multimedia big data Internet of things Multimedia forensics Region duplication detection Copy-move forgery Image segmentation LIOP 



This work is supported by the National Natural Science Foundation of China (No. U1736118), the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Key Project of Scientific Research Plan of Guangzhou (No. 201804020068), the Fundamental Research Funds for the Central Universities (No. 16lgjc83 and No. 17lgjc45), the Science and Technology Planning Project of Guangdong Province (Grant No.2017A040405051).


  1. 1.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S. (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282Google Scholar
  2. 2.
    Alcantarilla PF, Bartoli A, Davison AJ (2012) Kaze features. In: European conference on computer vision(ECCV), Florence, Italy, pp 214–227Google Scholar
  3. 3.
    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 Forensic Secur 6(3):1099–1110Google Scholar
  4. 4.
    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):659–669Google Scholar
  5. 5.
    Bashar M, Noda K, Ohnishi N, Mori K (2010) Exploring duplicated regions in natural images. IEEE Trans Image Process PP(99):1–1Google Scholar
  6. 6.
    Bay H, Ess A, Tuytelaars T, Gool LV (2008) SURF: speeded up robust features. Comput Vis Image Underst 110(3):346–359Google Scholar
  7. 7.
    Bedi G, Venayagamoorthy GK, Singh R, Brooks R, Wang KC (2018) Review of internet of things (iot) in electric power and energy systems. IEEE Int Things J PP(99):1–1Google Scholar
  8. 8.
    Bravo-Solorio S, Nandi AK (2011) Exposing duplicated regions affected by reflection, rotation and scaling. In: IEEE International conference on acoustics, speech and signal processing(ICASSP), Prague, Czech Republic, pp 1880–1883Google Scholar
  9. 9.
    Chen J, Lu W, Fang Y, Liu X, Yeung Y, Yingjie X (2018) Binary image steganalysis based on local texture pattern. J Vis Commun Image Represent 55:149–156Google Scholar
  10. 10.
    Chen J, Lu W, Yeung Y, Xue Y, Liu X, Lin C, Zhang Y (2018) Binary image steganalysis based on distortion level co-occurrence matrix. Comput Mater Continua 55(2):201–211Google Scholar
  11. 11.
    Chen L, Lu W, 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):244–254Google Scholar
  12. 12.
    Chen X, Weng J, Lu W, Xu J (2018) Multi-gait recognition based on attribute discovery. IEEE Trans Pattern Anal Mach Intell PP(99):1–1Google Scholar
  13. 13.
    Chen X, Weng J, Lu W, Xu J, Weng J (2017) Deep manifold learning combined with convolutional neural networks for action recognition. IEEE Trans Neural Netw Learn Syst PP(99):1–15Google Scholar
  14. 14.
    Christlein V, Riess C, Angelopoulou E (2010) On rotation invariance in copy-move forgery detection. In: EEE International workshop on information forensics and security (WIFS), Seattle, WA, USA , pp 1–6Google Scholar
  15. 15.
    Christlein V, Riess C, Jordan J, Riess C (2012) Angelopoulou, e.: an evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensic Secur 7(6):1841–1854Google Scholar
  16. 16.
    Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy-move forgery detection. IEEE Trans Inf Forensic Secur 10(11):2284–2297Google Scholar
  17. 17.
    Fang W, Li Y, Zhang H, Xiong N, Lai J, Vasilakos AV (2014) On the throughput-energy tradeoff for data transmission between cloud and mobile devices. Inf Sci 283(283):79–93Google Scholar
  18. 18.
    Fang Y, Fang Z, Yuan F, Yang Y, Yang S, Xiong NN (2017) Optimized multioperator image retargeting based on perceptual similarity measure. IEEE Trans Syst Man Cybern Syst 47(11):2956–2966Google Scholar
  19. 19.
    Feng B, Lu W, Sun W (2014) Secure binary image steganography based on minimizing the distortion on the texture. IEEE Trans Inf Forensic Secur 10(2):243–255Google Scholar
  20. 20.
    Feng B, Lu W, Sun W (2015) Binary image steganalysis based on pixel mesh markov transition matrix. J Vis Commun Image Represent 26:284–295Google Scholar
  21. 21.
    Feng B, Lu W, Sun W (2015) Novel steganographic method based on generalized k-distance n-dimensional pixel matching. Multimed Tools Appl 74(21):9623–9646Google Scholar
  22. 22.
    Feng B, Lu W, Sun W, Huang J, Shi YQ (2016) Robust image watermarking based on tucker decomposition and adaptive-lattice quantization index modulation. Signal Process Image Commun 41(C):1–14Google Scholar
  23. 23.
    Feng B, Weng J, Lu W, Pei B (2017) Steganalysis of content-adaptive binary image data hiding. J Vis Commun Image Represent 46:119–127. Google Scholar
  24. 24.
    Feng B, Weng J, Lu W, Pei B (2017) Multiple watermarking using multilevel quantization index modulation. In: International workshop on digital watermarking, Beijing, China, pp 312–326Google Scholar
  25. 25.
    Ferreira A, Felipussi SC, Alfaro C, Fonseca P, Vargasmunoz JE, Dos Santos JA, Rocha A (2016) Behavior knowledge space-based fusion for copy-move forgery detection. IEEE Trans Image Process 25(10):4729–4742MathSciNetzbMATHGoogle Scholar
  26. 26.
    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):381–395MathSciNetGoogle Scholar
  27. 27.
    Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. In: Proceeding of digital forensic research workshop, Cleveland, OH, USA, pp 19–23Google Scholar
  28. 28.
    Gao L, Yu F, Chen Q, Xiong N (2016) Consistency maintenance of do and undo/redo operations in real-time collaborative bitmap editing systems. Clust Comput 19(1):255–267Google Scholar
  29. 29.
    Ghorbani M, Firouzmand M, Faraahi A (2011) DWT-DCT (QCD) based copy-move image forgery detection. In: International conference on systems, signals and image processing, pp 1–4. SarajevoGoogle Scholar
  30. 30.
    Gui J, Hui L, Xiong N (2017) A game-based localized multi-objective topology control scheme in heterogeneous wireless networks. IEEE Access 5(99):2396–2416Google Scholar
  31. 31.
    Harris CG, Stephens MJ (1988) A combined corner and edge detector. In: Alvey vision conference, pp 147–151Google Scholar
  32. 32.
    Hu C, Xu Z, Liu Y, Mei L, Chen L, Luo X (2014) Semantic link network-based model for organizing multimedia big data. IEEE Trans Emerg Topics Comput 2(3):376–387Google Scholar
  33. 33.
    Huang H, Guo W, Zhang Y (2008) Detection of copy-move forgery in digital images using SIFT algorithm. In: IEEE Pacific-Asia workshop on computational intelligence and industrial application, pp 272–276Google Scholar
  34. 34.
    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):178–184Google Scholar
  35. 35.
    Jin G, Wan X (2017) An improved method for SIFT-based copy-move forgery detection using non-maximum value suppression and optimized J-Linkage. Signal Process Image Commun 57:113– 125Google Scholar
  36. 36.
    Lee JC, Chang CP, Chen WK (2015) Detection of copy-move image forgery using histogram of orientated gradients. Inf Sci 321(C):250–262Google Scholar
  37. 37.
    Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensic Secur 10(3):507–518Google Scholar
  38. 38.
    Li J, Lu W (2016) Blind image motion deblurring with L0-regularized priors. J Vis Commun Image Represent 40:14–23Google Scholar
  39. 39.
    Li J, Lu W, Weng J, Mao Y, Li G (2018) Double jpeg compression detection based on block statistics. Multimed Tools Appl 77(24):1–16Google Scholar
  40. 40.
    Li J, Yang F, Lu W, Sun W (2016) Keypoint-based copy-move detection scheme by adopting mscrs and improved feature matching. Multimed Tools Appl 76(20):1–15Google Scholar
  41. 41.
    Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224(1-3):59Google Scholar
  42. 42.
    Lin B, Guo W, Xiong N, Chen G, Vasilakos AV, Zhang H (2016) A pretreatment workflow scheduling approach for big data applications in multicloud environments. IEEE Trans Netw Serv Manag 13(3):581–594Google Scholar
  43. 43.
    Lin C, Lu W, Sun W, Zeng J, Xu T, Lai JH (2017) Region duplication detection based on image segmentation and keypoint contexts. Multimed Tools Appl 77(11):1–18Google Scholar
  44. 44.
    Liu G, Wang J, Lian S, Wang Z (2011) A passive image authentication scheme for detecting region-duplication forgery with rotation. J Netw Comput Appl 34(5):1557–1565Google Scholar
  45. 45.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110Google Scholar
  46. 46.
    Lu X, Tu L, Zhou X, Xiong N, Sun L (2017) Vimedianet: an emulation system for interactive multimedia based telepresence services. J Supercomput 73(8):3562–3578Google Scholar
  47. 47.
    Lu Z, Lin YR, Huang X, Xiong N, Fang Z (2017) Visual topic discovering, tracking and summarization from social media streams. Multimed Tools Appl 76(8):1–25Google Scholar
  48. 48.
    Ma Y, Luo X, Li X, Bao Z, Zhang Y (2018) Selection of rich model steganalysis features based on decision rough set α-positive region reduction. IEEE Trans Circ Syst Video Technol PP(99): 1–1Google Scholar
  49. 49.
    Mahdian B, Saic S (2007) Detection of copy-move forgery using a method based on blur moment invariants. Forensic Sci Int 171:180–189Google Scholar
  50. 50.
    Melro LS, Jensen LR (2017) Influence of functionalization on the structural and mechanical properties of graphene. Comput Mater Continua 53(2):111–131Google Scholar
  51. 51.
    Nelson B, Phillips A, Steuart C (2015) Guide to computer forensics and investigations delmar learningGoogle Scholar
  52. 52.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensic Secur 5(4):857–867Google Scholar
  53. 53.
    Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Tech. Rep. TR2004-515, Department of Computer Science Dartmouth CollegeGoogle Scholar
  54. 54.
    Pun CM, Yuan XC, Bi XL (2015) Image forgery detection using adaptive over-segmentation and feature points matching. IEEE Trans Inf Forensic Secur 10 (8):1705–1716Google Scholar
  55. 55.
    Ryu SJ, Kirchner M, Lee MJ, Lee HK (2013) Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans Inf Forensic Secur 8(8):1355–1370Google Scholar
  56. 56.
    Ryu SJ, Lee MJ, Lee HK (2010) Detection of copy-rotate-move forgery using Zernike moments. In: IEEE International workshop on information hiding(IH). Springer, Berlin, pp 51–65Google Scholar
  57. 57.
    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
  58. 58.
    Shu L, Fang Y, Fang Z, Yang Y, Fei F, Xiong N (2016) A novel objective quality assessment for super-resolution images. Int J Sig Process 9(5):297–308Google Scholar
  59. 59.
    Silva E, Carvalho T, Ferreira A, Rocha A (2015) Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes. J Vis Commun Image Represent 29(C):16–32Google Scholar
  60. 60.
    Vedaldi A, Fulkerson B (2010) Vlfeat: an open and portable library of computer vision algorithms. In: International conference on multimedea, Firenze, Italy, pp 1469–1472Google Scholar
  61. 61.
    Wang J, Li T, Shi YQ, Lian S, Ye J (2016) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed Tools Appl 76(22):1–17Google Scholar
  62. 62.
    Wang Y, Chen K, Yu J, Xiong N, Leung H, Zhou H, Zhu L (2017) Dynamic propagation characteristics estimation and tracking based on an em-ekf algorithm in time-variant mimo channel. Inf Sci 408(C):70–83Google Scholar
  63. 63.
    Wang Z, Fan B, Wu F (2011) Local intensity order pattern for feature description. In: IEEE International conference on computer vision (ICCV), pp 603–610Google Scholar
  64. 64.
    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 Image Represent 46:219–232Google Scholar
  65. 65.
    Wu P, Xiao F, Sha C, Huang H, Wang R, Xiong N (2017) Node scheduling strategies for achieving full-view area coverage in camera sensor networks. Sensors 17(6):1303Google Scholar
  66. 66.
    Xia Z, Wang X, Sun X, Liu Q, Xiong N (2016) Steganalysis of lsb matching using differences between nonadjacent pixels. Multimed Tools Appl 75(4):1947–1962Google Scholar
  67. 67.
    Xia Z, Xiong NN, Vasilakos AV, Sun X (2017) Epcbir: an efficient and privacy-preserving content-based image retrieval scheme in cloud computing. Inf Sci 387:195–204Google Scholar
  68. 68.
    Xiong N, Jia X, Yang LT, Vasilakos AV, Li Y, Pan Y (2010) A distributed efficient flow control scheme for multirate multicast networks. IEEE Trans Parallel Distrib Syst 21(9):1254–1266Google Scholar
  69. 69.
    Xiong N, Liu RW, Liang M, Wu D, Liu Z, Wu H (2017) Effective alternating direction optimization methods for sparsity-constrained blind image deblurring. Sensors 17(1):1–27Google Scholar
  70. 70.
    Xiong N, Vasilakos AV, Yang LT, Song L, Pan Y, Kannan R, Li Y (2009) Comparative analysis of quality of service and memory usage for adaptive failure detectors in healthcare systems. IEEE J Sel Areas Commun 27(4):495–509Google Scholar
  71. 71.
    Xiong N, Vasilakos AV, Yang LT, Wang CX, Kannan R, Chang CC, Pan Y (2009) A novel self-tuning feedback controller for active queue management supporting tcp flows. Inf Sci 180(11):2249–2263MathSciNetGoogle Scholar
  72. 72.
    Xu B, Wang J, Liu G, Dai Y (2010) Image copy-move forgery detection based on SURF. In: International conference on multimedia information networking and security (MINES), Nanjing, China, pp 889– 892Google Scholar
  73. 73.
    Yang B, Sun X, Chen X, Zhang J, Li X (2013) An efficient forensic method for copy-move forgery detection based on dwt-fwht. Radioengineering 22(4):1098–1105Google Scholar
  74. 74.
    Yang F, Li J, Lu W, Weng J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intell 59:73–83Google Scholar
  75. 75.
    Yang Y, Tong S, Huang S, Lin P (2014) Dual-tree complex wavelet transform and image block residual-based multi-focus image fusion in visual sensor networks. Sensors 14(12):22,408–22,430Google Scholar
  76. 76.
    Yang Z, Ma L, Ma Q, Cui J, Nie Y, Dong H, An X (2017) Multiscale nonlinear thermo-mechanical coupling analysis of composite structures with quasi-periodic properties. Comput Mater Continua 53(3):219–248Google Scholar
  77. 77.
    Zhang C, Wu D, Liu RW, Xiong N (2015) Non-local regularized variational model for image deblurring under mixed gaussian-impulse noise. J Int Technol 16(7):1301–1319Google Scholar
  78. 78.
    Zhang F, Lu W, Liu H, Xue F (2018) Natural image deblurring based on l0-regularization and kernel shape optimization. Multimed Tools Appl 77(20):1–19Google Scholar
  79. 79.
    Zhang H, Liu RW, Wu D, Liu Y, Xiong NN (2016) Non-convex total generalized variation with spatially adaptive regularization parameters for edge-preserving image restoration. J Int Technol 17(7):1391–1403Google Scholar
  80. 80.
    Zhang Q, Lu W, Wang R, Li G (2018) Digital image splicing detection based on markov features in block dwt domain. Multimed Tools Appl 77(23):1–22Google Scholar
  81. 81.
    Zhang Q, Lu W, Weng J (2016) Joint image splicing detection in dct and contourlet transform domain. J Vis Commun Image Represent 40:449–458Google Scholar
  82. 82.
    Zhang Y, Qin C, Zhang W, Liu F, Luo X (2018) On the fault-tolerant performance for a class of robust image steganography. Sig Process 146:1–1Google Scholar
  83. 83.
    Zheng H, Guo W, Xiong N (2017) A kernel-based compressive sensing approach for mobile data gathering in wireless sensor network systems. IEEE Trans Syst Man Cybern Syst PP(99):1–13Google Scholar
  84. 84.
    Zhou P, Zhou Y, Wu D, Jin H (2016) Differentially private online learning for cloud-based video recommendation with multimedia big data in social networks. IEEE Trans Multimed 18(6):1217–1229Google Scholar
  85. 85.
    Zhou Y, Zhang D, Xiong N (2017) Post-cloud computing paradigms: a survey and comparison. Tsinghua Sci Technol 22(6):714–732Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.School of Data and Computer Science, Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhouChina
  2. 2.Center for Faculty Development and Educational TechnologyGuangdong University of Finance and EconomicsGuangzhouChina
  3. 3.School of Electronics and Information Technology, Key Laboratory of Information Technology (Ministry of Education)Sun Yat-sen UniversityGuangzhouChina
  4. 4.State Key Laboratory of Information Security, Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  5. 5.School of ComputingEdinburgh Napier UniversityEdinburghUK

Personalised recommendations