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

  • Bin Yang
  • Xingming Sun
  • Honglei Guo
  • Zhihua Xia
  • Xianyi Chen
Article

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.

Keywords

Image forensics Copy-move forgery detection SIFT Descriptor Detector Tempering 

References

  1. 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:1–17Google Scholar
  2. 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 2:34–48Google Scholar
  3. 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 6:1099–1110CrossRefGoogle Scholar
  4. 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 2013:1–17CrossRefGoogle Scholar
  5. 5.
    Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110:346–359CrossRefGoogle Scholar
  6. 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 345:226–242CrossRefGoogle Scholar
  7. 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 51:124–144MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Chen Y, Hao C, Wu W, Wu E (2016) Robust dense reconstruction by range merging based on confidence estimation. SCIENCE CHINA Inf Sci 59:092103CrossRefGoogle Scholar
  9. 9.
    Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy–move forgery detection. IEEE Transactions on Information Forensics and Security 10:2284–2297CrossRefGoogle Scholar
  10. 10.
    Fridrich J, Soukal BD, Lukáš AJ (2003) Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop, Cleveland, OHGoogle Scholar
  11. 11.
    Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer, Berlin. Springer series in statisticsGoogle Scholar
  12. 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 11:2706–2716CrossRefGoogle Scholar
  13. 13.
    Gu B, Sheng VS (2016) A robust regularization path algorithm for v-support vector classification. IEEE Trans Neural Netw Learn Syst PP: 1–8Google Scholar
  14. 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 26:1403–1416MathSciNetCrossRefGoogle Scholar
  15. 15.
    Guo J-M, Liu Y-F, Wu Z-J (2013) Duplication forgery detection using improved DAISY descriptor. Expert Syst Appl 40:707–714CrossRefGoogle Scholar
  16. 16.
    Kakar P, Sudha N (2012) Exposing Postprocessed copy-paste forgeries through transform-invariant features. IEEE Transactions on Information Forensics and Security 7:1018–1028CrossRefGoogle Scholar
  17. 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. 1–5Google Scholar
  18. 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 25:2201–2210CrossRefGoogle Scholar
  19. 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 10:507–518CrossRefGoogle Scholar
  20. 20.
    Liao K, Liu G, Hui Y (2013) An improvement to the SIFT descriptor for image representation and matching. Pattern Recogn Lett 34:1211–1220CrossRefGoogle Scholar
  21. 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 15:415–434CrossRefGoogle Scholar
  22. 22.
    Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110CrossRefGoogle Scholar
  23. 23.
    Luo J, Oubong G (2009) A comparison of SIFT, PCA-SIFT and SURF. International Journal of Image Processing 3:143–152Google Scholar
  24. 24.
    Muhammad G, Hussain M, Bebis G (2012) Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit Investig 9:49–57CrossRefGoogle Scholar
  25. 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. 1–4Google Scholar
  26. 26.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Transactions on Information Forensics and Security 5:857–867CrossRefGoogle Scholar
  27. 27.
    Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53:758–767MathSciNetCrossRefGoogle Scholar
  28. 28.
    Quackenbush J (2001) Computational analysis of microarray data. Nat Rev Genet 2:418–427CrossRefGoogle Scholar
  29. 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 17:453–462CrossRefGoogle Scholar
  30. 30.
    Shivakumar BL, Baboo S (2011) Detection of region duplication forgery in digital images using SURF. International Journal of Computer Science Issues 8:199–205Google Scholar
  31. 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. 49–54Google Scholar
  32. 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:1–17Google Scholar
  33. 33.
    Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406CrossRefGoogle Scholar
  34. 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 SecurGoogle Scholar
  35. 35.
    Xia Z, Lv R, Zhu Y, Ji P, Sun H, Shi Y-Q (2016b) Fingerprint liveness detection using gradient-based texture features. SIViP:1–8Google Scholar
  36. 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 75:1947–1962Google Scholar
  37. 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 22:1098–1105Google Scholar
  38. 38.
    Yang B, Sun X, Chen X, Zhang J, Li X (2014) Exposing photographic splicing by detecting the inconsistencies in shadows. Comput J 58:588–600CrossRefGoogle Scholar
  39. 39.
    Yanhua Z, Sun X, Baowei W (2016) Efficient algorithm for k-barrier coverage based on integer linear programming. China Communications 13:16–23CrossRefGoogle Scholar
  40. 40.
    Yap KH, Miao Z (2015) Hybrid feature-based wallpaper visual search. In: 2015 I.E. International Symposium on Circuits and Systems (ISCAS), pp. 730–733Google Scholar
  41. 41.
    Yuan C, Sun X, Lv R (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Communications 13(7):60–65CrossRefGoogle Scholar
  42. 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 28:961–973Google Scholar
  43. 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:1531–1540CrossRefGoogle Scholar
  44. 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 12:48–63Google Scholar
  45. 45.
    Zhu Y, Shen X, Chen H (2015) Copy-move forgery detection based on scaled ORB. Multimedia Tools and Applications:1–13Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Bin Yang
    • 1
  • Xingming Sun
    • 2
  • Honglei Guo
    • 1
  • Zhihua Xia
    • 2
  • Xianyi Chen
    • 2
  1. 1.School of DesignJiangnan UniversityWuxiChina
  2. 2.Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science and TechnologyNanjingChina

Personalised recommendations