A new keypoint-based copy-move forgery detection for small smooth regions

Article

Abstract

Copy-move forgery is one of the most common types of image forgeries, where a region from one part of an image is copied and pasted onto another part, thereby concealing the image content in the latter region. Keypoint based copy-move forgery detection approaches extract image feature points and use local visual features, rather than image blocks, to identify duplicated regions. Keypoint based approaches exhibit remarkable performance with respect to computational cost, memory requirement, and robustness. But unfortunately, they usually do not work well if smooth background areas are used to hide small objects, as image keypoints cannot be extracted effectively from those areas. It is a challenging work to design a keypoint-based method for detecting forgeries involving small smooth regions. In this paper, we propose a new keypoint-based copy-move forgery detection for small smooth regions. Firstly, the original tampered image is segmented into nonoverlapping and irregular superpixels, and the superpixels are classified into smooth, texture and strong texture based on local information entropy. Secondly, the stable image keypoints are extracted from each superpixel, including smooth, texture and strong texture ones, by utilizing the superpixel content based adaptive feature points detector. Thirdly, the local visual features, namely exponent moments magnitudes, are constructed for each image keypoint, and the best bin first and reversed generalized 2 nearest-neighbor algorithm are utilized to find rapidly the matching image keypoints. Finally, the falsely matched image keypoints are removed by customizing the random sample consensus, and the duplicated regions are localized by using zero mean normalized cross-correlation measure. Extensive experimental results show that the newly proposed scheme can achieve much better detection results for copy-move forgery images under various challenging conditions, such as geometric transforms, JPEG compression, and additive white Gaussian noise, compared with the existing state-of-the-art copy-move forgery detection methods.

Keywords

Copy-move forgery detection Superpixel Adaptive feature points detector Exponent moments Reversed generalized 2 nearest-neighbor 

References

  1. 1.
    Al-Qershi OM, BE Khoo (2015) Enhanced matching method for copy-move forgery detection by means of Zernike moments. 13th International Workshop on Digital-Forensics and Watermarking (IWDW 2014), LNCS 9023, pp 485–497Google Scholar
  2. 2.
    Amerini I, Ballan L, Caldelli R, Del Bimbo A, Del Tongo L, 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.
    Amerini I, Ballan L, Caldelli R (2011) A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6(3):1099–1110CrossRefGoogle Scholar
  4. 4.
    Ardizzone E, Bruno A, Mazzola G (2015) Copy-move forgery detection by matching triangles of keypoints. IEEE Trans Inf Forensics Secur 10(10):2084–2094CrossRefGoogle Scholar
  5. 5.
    Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded up robust features (SURF). Comput Vis Image Underst 110(3):346–359CrossRefGoogle Scholar
  6. 6.
    Bi X, Pun CM, Yuan XC (2016) Multi-level dense descriptor and hierarchical feature matching for copy-move forgery detection. Inf Sci 345:226–242CrossRefGoogle Scholar
  7. 7.
    Bravo-Solorio S, Nandi AK (2011) Exposing duplicated regions affected by reflection, rotation and scaling. 2011 I.E. International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, pp 1880–1883Google Scholar
  8. 8.
    Caldelli R, Amerini I, Ballan L (2012) On the effectiveness of local warping against SIFT-based copy-move detection. Proceedings of the 5th International Symposium on Communications, Control and Signal Processing, Rome, Italy, pp 1–5Google Scholar
  9. 9.
    Chambers J, Yan W, Garhwal A (2015) Currency security and forensics: a survey. Multimedia Tools Appl 74(11):4013–4043CrossRefGoogle Scholar
  10. 10.
    Chen L, Lu W, Ni J, Sun W (2013) Region duplication detection based on Harris corner points and step sector statistics. J Vis Commun Image Represent 24(3):244–254CrossRefGoogle Scholar
  11. 11.
    Chen B, Shu H, Coatrieux G (2015) Color image analysis by quaternion-type moments. J Math Imaging Vision 51(1):124–144MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Christlein V, Riess C, Jordan J (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensics Secur 7(6):1841–1854CrossRefGoogle Scholar
  13. 13.
    Costanzo A, Amerini I, Caldelli R (2014) Forensic analysis of SIFT keypoint removal and injection. IEEE Trans Inf Forensics Secur 9(9):1450–1464CrossRefGoogle Scholar
  14. 14.
    Cozzolino D, Poggi G, Verdoliva L (2014) Copy-move forgery detection based on patchmatch. 2014 I.E. International Conference on Image Processing (ICIP), Paris, France, pp 5312–5316Google Scholar
  15. 15.
    Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy-move forgery detection. IEEE Trans Inf Forensics Secur 10(11):2284–2297CrossRefGoogle Scholar
  16. 16.
    Da S (2009) Research on density based image processing algorithms and application. Harbin Institute of Technology, HarbinGoogle Scholar
  17. 17.
    Davarzani R, Yaghmaie K, Mozaffari S (2013) Copy-move forgery detection using multiresolution local binary patterns. Forensic Sci Int 231(1–3):61–72CrossRefGoogle Scholar
  18. 18.
    Fattah SA, Ullah MMI, Ahmed M (2014) A scheme for copy-move forgery detection in digital images based on 2D-DWT. IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS), College Station, TX, pp 801–804Google Scholar
  19. 19.
    Imamoglu M, Ulutas G, Ulutas M (2013) Detection of copy-move forgery using Krawtchouk moment. 2013 8th International Conference on Electrical and Electronics Engineering, Bursa, Turkey, pp 311–314Google Scholar
  20. 20.
    Jaberi M, Bebis G, Hussain M (2014) Accurate and robust localization of duplicated region in copy-move image forgery. Mach Vis Appl 25(2):451–475CrossRefGoogle Scholar
  21. 21.
    Jie Z, Guo J (2013) Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Sci Int 233(1–3):158–166Google Scholar
  22. 22.
    Kakar P, Sudha N (2012) Exposing postprocessed copy-paste forgeries through transform-invariant features. IEEE Trans Inf Forensics Secur 7(3):1018–1028CrossRefGoogle Scholar
  23. 23.
    Ketenci S, Ulutas G, Ulutas M (2014) Detection of duplicated regions in images using 1D-Fourier transform. International Conference on Systems, Signals and Image Processing, Dubrovnik, Croatia, pp 171–174Google Scholar
  24. 24.
    Lee JC (2015) Copy-move image forgery detection based on Gabor magnitude. J Vis Commun Image Represent 31:320–334CrossRefGoogle Scholar
  25. 25.
    Lee J, Chang C, Chen W (2015) Detection of copy–move image forgery using histogram of orientated gradients. Inf Sci 321:250–262CrossRefGoogle Scholar
  26. 26.
    Li J, Li X, Yang B (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518CrossRefGoogle Scholar
  27. 27.
    Li Y, Wang S, Tian Q (2015) A survey of recent advances in visual feature detection. Neurocomputing 149:736–751CrossRefGoogle Scholar
  28. 28.
    Liu MY, Tuzel O, Ramalingam S, Chellappa R (2011) Entropy rate superpixel segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, Colorado, USA, pp 2097–2104Google Scholar
  29. 29.
    Meng M, Ping ZL (2011) Decompose and reconstruct images based on exponential Fourier moments. J Inner Mongolia Norm Univ (Nat Sci Ed) 40(3):258–260Google Scholar
  30. 30.
    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
  31. 31.
    Pun CM, Yuan XC, Bi XL (2015) Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Trans Inf Forensics Secur 10(8):1705–1716CrossRefGoogle Scholar
  32. 32.
    Qureshi MA, Deriche M (2015) A bibliography of pixel-based blind image forgery detection techniques. Signal Process Image Commun 39(Part A):46–74CrossRefGoogle Scholar
  33. 33.
    Ryu SJ, Kirchner M, Lee MJ (2013) Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans Inf Forensics Secur 8(8):1355–1370CrossRefGoogle Scholar
  34. 34.
    Silva E, Carvalho T, Ferreira A (2015) Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes. J Vis Commun Image Represent 29:16–32CrossRefGoogle Scholar
  35. 35.
    Sitara K, Mehtre BM (2016) Digital video tampering detection: an overview of passive techniques. Digit Investig 18:8–22CrossRefGoogle Scholar
  36. 36.
    Ustubioglu B, Ayas S, Doganl H (2015) Image forgery detection based on color SIFT. The IEEE Signal Processing and Communications Applications Conference, Malatya, Turkey, pp 1741–1744Google Scholar
  37. 37.
    Wang X, Liu Y, Li S, Yang H, Niu P, Zhang Y (2015) A new robust digital image watermarking using local polar harmonic transform. Comput Electr Eng 46:403–418CrossRefGoogle Scholar
  38. 38.
    Wu YJ, Yu D, Duan HB (2014) Dual tree complex wavelet transform approach to copy-rotate-move forgery detection. Sci China Inf Sci 57(1):1–12Google Scholar
  39. 39.
    Xia Z, Wang X, Sun X (2016) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimedia Tools and Applications 75(4):1947–1962CrossRefGoogle Scholar
  40. 40.
    Yan L, Nian L, Bin Z, Kai-guo Y, Yang Y (2015) Image multiple copy-move forgery detection algorithm based on reversed-generalized 2 nearest-neighbor. J Electron Inf Technol 7:1767–1773Google Scholar
  41. 41.
    Yu L, Han Q, Niu X (2016) Feature point-based copy-move forgery detection: covering the non-textured areas. Multimedia Tools Appl 75(2):1159–1176CrossRefGoogle Scholar
  42. 42.
    Zhou Z, Wang Y, Jonathan Wu QM, Yang C-N, Sun X (2006) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Secur. doi:10.1109/TIFS.2016.2601065 Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalianPeople’s Republic of China
  2. 2.Jiangsu Engineering Center of Network Monitoring & School of Computer and SoftwareNanjing University of Information Science & Technology (NUIST)NanjingChina

Personalised recommendations