Skip to main content
SpringerLink
Log in

A copy-move forgery detection method based on CMFD-SIFT

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

Abstract

A very common way of image tampering is the copy-move attack. When creating a copy-move forgery, it is often necessary to add or remove important objects from an image. To carry out forensic analysis of such images, various copy-move forgery detection (CMFD) methods have been developed in the literatures. In recent years, many feature-based CMFD approaches have emerged due to its excellent robustness to various transformations. However there is still place to improve performance further. Many of them would suffer from the problem of insufficient matched key-points while performing on the mirror transformed forgeries. Furthermore, many feature-based methods might hardly expose the tempering when the forged region is of uniform texture. In this paper, a novel feature-based CMFD method is proposed. Key-points are detected by using a modified SIFT-based detector. A novel key-points distribution strategy is developed for interspersing the key-points evenly throughout an image. Finally, key-points are descripted by an improved SIFT descriptor which is enhanced for the CMFD scenario. Extensive experimental results are presented to confirm the efficacy.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Ahmad J, Sajjad M, Mehmood I, Rho S, Baik SW (2015a) Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems. J Real-Time Image Proc:1–17

  2. Ahmad J, Sajjad M, Mehmood I, Rho S, Baik SW (2015b) Describing colors, textures and shapes for content based image retrieval - a survey. Journal of Platform Technology 2:34–48

    Google Scholar 

  3. Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security 6:1099–1110

    Article  Google Scholar 

  4. Amerini I, Barni M, Caldelli R, Costanzo A (2013) Counter-forensics of SIFT-based copy-move detection by means of keypoint classification. EURASIP Journal on Image and Video Processing 2013:1–17

    Article  Google Scholar 

  5. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110:346–359

    Article  Google Scholar 

  6. Bi X, Pun C-M, Yuan X-C (2016) Multi-level dense descriptor and hierarchical feature matching for copy–move forgery detection. Inf Sci 345:226–242

    Article  Google Scholar 

  7. Chen B, Shu H, Coatrieux G, Chen G, Sun X, Coatrieux JL (2015) Color image analysis by quaternion-type moments. Journal of Mathematical Imaging and Vision 51:124–144

    Article  MathSciNet  MATH  Google Scholar 

  8. Chen Y, Hao C, Wu W, Wu E (2016) Robust dense reconstruction by range merging based on confidence estimation. SCIENCE CHINA Inf Sci 59:092103

    Article  Google Scholar 

  9. Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy–move forgery detection. IEEE Transactions on Information Forensics and Security 10:2284–2297

    Article  Google Scholar 

  10. Fridrich J, Soukal BD, Lukáš AJ (2003) Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop, Cleveland, OH

  11. Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer, Berlin. Springer series in statistics

  12. Fu Z, Wu X, Guan C, Sun X, Ren K (2016) Toward efficient multi-keyword fuzzy search over encrypted outsourced data with accuracy improvement. IEEE Transactions on Information Forensics and Security 11:2706–2716

    Article  Google Scholar 

  13. Gu B, Sheng VS (2016) A robust regularization path algorithm for v-support vector classification. IEEE Trans Neural Netw Learn Syst PP: 1–8

  14. Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Transactions on Neural Networks and Learning Systems 26:1403–1416

    Article  MathSciNet  Google Scholar 

  15. Guo J-M, Liu Y-F, Wu Z-J (2013) Duplication forgery detection using improved DAISY descriptor. Expert Syst Appl 40:707–714

    Article  Google Scholar 

  16. Kakar P, Sudha N (2012) Exposing Postprocessed copy-paste forgeries through transform-invariant features. IEEE Transactions on Information Forensics and Security 7:1018–1028

    Article  Google Scholar 

  17. Khuspe K, Mane V (2015) Robust image forgery localization and recognition in copy-move using bag of features and SVM. In: 2015 International Conference on Communication, Information & Computing Technology (ICCICT), pp. 1–5

  18. Li J, Huang X, Li J, Chen X, Xiang Y (2014) Securely outsourcing attribute-based encryption with Checkability. IEEE Transactions on Parallel and Distributed Systems 25:2201–2210

    Article  Google Scholar 

  19. Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Transactions on Information Forensics and Security 10:507–518

    Article  Google Scholar 

  20. Liao K, Liu G, Hui Y (2013) An improvement to the SIFT descriptor for image representation and matching. Pattern Recogn Lett 34:1211–1220

    Article  Google Scholar 

  21. Lindeberg T, Gårding J (1997) Shape-adapted smoothing in estimation of 3-D shape cues from affine deformations of local 2-D brightness structure. Image Vis Comput 15:415–434

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Luo J, Oubong G (2009) A comparison of SIFT, PCA-SIFT and SURF. International Journal of Image Processing 3:143–152

    Google Scholar 

  24. Muhammad G, Hussain M, Bebis G (2012) Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit Investig 9:49–57

    Article  Google Scholar 

  25. Neamtu C, Barca C, Achimescu E, Gavriloaia B (2013) Exposing copy-move image tampering using forensic method based on SURF. In: 2013 International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–4

  26. Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Transactions on Information Forensics and Security 5:857–867

    Article  Google Scholar 

  27. Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53:758–767

    Article  MathSciNet  MATH  Google Scholar 

  28. Quackenbush J (2001) Computational analysis of microarray data. Nat Rev Genet 2:418–427

    Article  Google Scholar 

  29. Shen J, Tan H, Moh S, Chung I, Liu Q, Sun X (2015) Enhanced secure sensor association and key management in wireless body area networks. Journal of Communications and Networks 17:453–462

    Article  Google Scholar 

  30. Shivakumar BL, Baboo S (2011) Detection of region duplication forgery in digital images using SURF. International Journal of Computer Science Issues 8:199–205

    Google Scholar 

  31. Tralic D, Zupancic I, Grgic S, Grgic M (2013) CoMoFoD—New database for copy-move forgery detection. In: ELMAR, 2013 55th International Symposium, pp. 49–54

  32. Wang J, Li T, Shi Y-Q, Lian S, Ye J (2016) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimedia Tools and Applications:1–17

  33. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406

    Article  Google Scholar 

  34. Xia Z, Wang X, Zhang L, Qin Z, Sun X, Ren K (2016a) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forensics Secur

  35. Xia Z, Lv R, Zhu Y, Ji P, Sun H, Shi Y-Q (2016b) Fingerprint liveness detection using gradient-based texture features. SIViP:1–8

  36. Xia Z, Wang X, Sun X, Liu Q, Xiong N (2016c) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimedia Tools and Applications 75:1947–1962

  37. 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 22:1098–1105

    Google Scholar 

  38. Yang B, Sun X, Chen X, Zhang J, Li X (2014) Exposing photographic splicing by detecting the inconsistencies in shadows. Comput J 58:588–600

    Article  Google Scholar 

  39. Yanhua Z, Sun X, Baowei W (2016) Efficient algorithm for k-barrier coverage based on integer linear programming. China Communications 13:16–23

    Article  Google Scholar 

  40. Yap KH, Miao Z (2015) Hybrid feature-based wallpaper visual search. In: 2015 I.E. International Symposium on Circuits and Systems (ISCAS), pp. 730–733

  41. Yuan C, Sun X, Lv R (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Communications 13(7):60–65

    Article  Google Scholar 

  42. Zheng Y, Jeon B, Xu D, Wu QMJ, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28:961–973

    Google Scholar 

  43. Zhou Z, Yang C-N, Chen B, Sun X, Liu Q, Wu QMJ (2016) Effective and efficient image copy detection with resistance to arbitrary rotation. IEICE Trans Inf Syst E99-D:1531–1540

    Article  Google Scholar 

  44. Zhou Z, Wang Y, Wu J, Yang CN, Sun X (2017) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Secur 12:48–63

  45. Zhu Y, Shen X, Chen H (2015) Copy-move forgery detection based on scaled ORB. Multimedia Tools and Applications:1–13

Download references

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (NO. U1536206, 61232016, U1405254, 61373133, 61502242, 61672294, 61602253); the Fundamental Research Funds for the Central Universities (NO. JUSRP11534); the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET)

Author information

Authors and Affiliations

  1. School of Design, Jiangnan University, NO.1800, Wuxi, 214122, China

    Bin Yang & Honglei Guo

  2. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Xingming Sun, Zhihua Xia & Xianyi Chen

Authors
  1. Bin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xingming Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Honglei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhihua Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xianyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, B., Sun, X., Guo, H. et al. A copy-move forgery detection method based on CMFD-SIFT. Multimed Tools Appl 77, 837–855 (2018). https://doi.org/10.1007/s11042-016-4289-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4289-y

Keywords

Search

Navigation