Skip to main content
SpringerLink
Log in

Copy-move forgery detection using combined features and transitive matching

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  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):2274–2282

    Google Scholar 

  2. Alcantarilla PF, Bartoli A, Davison AJ (2012) Kaze features. In: European conference on computer vision(ECCV), Florence, Italy, pp 214–227

  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):1099–1110

    Google Scholar 

  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):659–669

    Google Scholar 

  5. Bashar M, Noda K, Ohnishi N, Mori K (2010) Exploring duplicated regions in natural images. IEEE Trans Image Process PP(99):1–1

    Google Scholar 

  6. Bay H, Ess A, Tuytelaars T, Gool LV (2008) SURF: speeded up robust features. Comput Vis Image Underst 110(3):346–359

    Google Scholar 

  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):1–1

    Google Scholar 

  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 1880–1883

  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 55:149–156

    Google Scholar 

  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):201–211

    Google Scholar 

  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):244–254

    Google Scholar 

  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):1–1

    Google Scholar 

  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):1–15

    Google Scholar 

  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 1–6

  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):1841–1854

    Google Scholar 

  16. Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy-move forgery detection. IEEE Trans Inf Forensic Secur 10(11):2284–2297

    Google Scholar 

  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):79–93

    Google Scholar 

  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):2956–2966

    Google Scholar 

  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):243–255

    Google Scholar 

  20. Feng B, Lu W, Sun W (2015) Binary image steganalysis based on pixel mesh markov transition matrix. J Vis Commun Image Represent 26:284–295

    Google Scholar 

  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):9623–9646

    Google Scholar 

  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):1–14

    Google Scholar 

  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. https://www.sciencedirect.com/science/article/pii/S1047320317300081

    Google Scholar 

  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 312–326

  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):4729–4742

    MathSciNet  MATH  Google Scholar 

  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):381–395

    MathSciNet  Google Scholar 

  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 19–23

  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):255–267

    Google Scholar 

  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. Sarajevo

  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):2396–2416

    Google Scholar 

  31. Harris CG, Stephens MJ (1988) A combined corner and edge detector. In: Alvey vision conference, pp 147–151

  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):376–387

    Google Scholar 

  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 272–276

  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):178–184

    Google Scholar 

  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– 125

    Google Scholar 

  36. Lee JC, Chang CP, Chen WK (2015) Detection of copy-move image forgery using histogram of orientated gradients. Inf Sci 321(C):250–262

    Google Scholar 

  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):507–518

    Google Scholar 

  38. Li J, Lu W (2016) Blind image motion deblurring with L0-regularized priors. J Vis Commun Image Represent 40:14–23

    Google Scholar 

  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):1–16

    Google Scholar 

  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):1–15

    Google Scholar 

  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):59

    Google Scholar 

  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):581–594

    Google Scholar 

  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):1–18

    Google Scholar 

  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):1557–1565

    Google Scholar 

  45. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Google Scholar 

  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):3562–3578

    Google Scholar 

  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):1–25

    Google Scholar 

  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): 1–1

    Google Scholar 

  49. Mahdian B, Saic S (2007) Detection of copy-move forgery using a method based on blur moment invariants. Forensic Sci Int 171:180–189

    Google Scholar 

  50. Melro LS, Jensen LR (2017) Influence of functionalization on the structural and mechanical properties of graphene. Comput Mater Continua 53(2):111–131

    Google Scholar 

  51. Nelson B, Phillips A, Steuart C (2015) Guide to computer forensics and investigations delmar learning

  52. Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensic Secur 5(4):857–867

    Google Scholar 

  53. Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Tech. Rep. TR2004-515, Department of Computer Science Dartmouth College

  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):1705–1716

    Google Scholar 

  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):1355–1370

    Google Scholar 

  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 51–65

    Google Scholar 

  57. Shivakumar BL, Baboo S (2011) Detection of region duplication forgery in digital images using SURF. Int J Comput Sci Issues 8(4):199–205

    Google Scholar 

  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):297–308

    Google Scholar 

  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):16–32

    Google Scholar 

  60. Vedaldi A, Fulkerson B (2010) Vlfeat: an open and portable library of computer vision algorithms. In: International conference on multimedea, Firenze, Italy, pp 1469–1472

  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):1–17

    Google Scholar 

  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):70–83

    Google Scholar 

  63. Wang Z, Fan B, Wu F (2011) Local intensity order pattern for feature description. In: IEEE International conference on computer vision (ICCV), pp 603–610

  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 46:219–232

    Google Scholar 

  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):1303

    Google Scholar 

  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):1947–1962

    Google Scholar 

  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 387:195–204

    Google Scholar 

  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):1254–1266

    Google Scholar 

  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):1–27

    Google Scholar 

  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):495–509

    Google Scholar 

  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):2249–2263

    MathSciNet  Google Scholar 

  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– 892

  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):1098–1105

    Google Scholar 

  74. Yang F, Li J, Lu W, Weng J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intell 59:73–83

    Google Scholar 

  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):22,408–22,430

    Google Scholar 

  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):219–248

    Google Scholar 

  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):1301–1319

    Google Scholar 

  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):1–19

    Google Scholar 

  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):1391–1403

    Google Scholar 

  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):1–22

    Google Scholar 

  81. Zhang Q, Lu W, Weng J (2016) Joint image splicing detection in dct and contourlet transform domain. J Vis Commun Image Represent 40:449–458

    Google Scholar 

  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 146:1–1

    Google Scholar 

  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):1–13

    Google Scholar 

  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):1217–1229

    Google Scholar 

  85. Zhou Y, Zhang D, Xiong N (2017) Post-cloud computing paradigms: a survey and comparison. Tsinghua Sci Technol 22(6):714–732

    Google Scholar 

Download references

Acknowledgments

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).

Author information

Authors and Affiliations

  1. School of Data and Computer Science, Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Cong Lin, Wei Lu & Xinchao Huang

  2. Center for Faculty Development and Educational Technology, Guangdong University of Finance and Economics, Guangzhou, 510320, China

    Cong Lin & Hanhui Lin

  3. School of Electronics and Information Technology, Key Laboratory of Information Technology (Ministry of Education), Sun Yat-sen University, Guangzhou, 510006, China

    Ke Liu & Wei Sun

  4. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Wei Lu

  5. School of Computing, Edinburgh Napier University, Edinburgh, EH10 5DT, UK

    Zhiyuan Tan

Authors
  1. Cong Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinchao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ke Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hanhui Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhiyuan Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Lu.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, C., Lu, W., Huang, X. et al. Copy-move forgery detection using combined features and transitive matching. Multimed Tools Appl 78, 30081–30096 (2019). https://doi.org/10.1007/s11042-018-6922-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6922-4

Keywords

Search

Navigation