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Robust copy–move forgery detection using quaternion exponent moments

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Abstract

The detection of forgeries in color images is a very important topic in forensic science. Copy–move (or copy–paste) forgery is the most common form of tampering associated with color images. Conventional copy–move forgeries detection techniques usually suffer from the problems of false positives and susceptibility to many signal processing operations. It is a challenging work to design a robust copy–move forgery detection method. In this paper, we present a novel block-based robust copy–move forgery detection approach using invariant quaternion exponent moments (QEMs). Firstly, original tempered color image is preprocessed with Gaussian low-pass filter, and the filtered color image is divided into overlapping circular blocks. Then, the accurate and robust feature descriptor, QEMs modulus, is extracted from color image block holistically as a vector field. Finally, exact Euclidean locality sensitive hashing is utilized to find rapidly the matching blocks, and the falsely matched block pairs are removed by customizing the random sample consensus with QEMs magnitudes differences. Extensive experimental results show the efficacy of the newly proposed approach in detecting copy–paste forgeries under various challenging conditions, such as noise addition, lossy compression, scaling, and rotation. We obtain the average forgery detection accuracy (F-measure) in excess of 96 and 88% across postprocessing operations, at image level and at pixel level, respectively.

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References

  1. Muhammad AQ, Mohamed D (2015) A bibliography of pixel-based blind image forgery detection techniques. Signal Process Image Commun 39(Part A):46–74

    Google Scholar 

  2. Christlein V, Riess C, Jordan J (2012) An evaluation of popular copy–move forgery detection approaches. IEEE Trans Inf Forensics Secur 7(6):1841–1854

    Article  Google Scholar 

  3. Chambers J, Yan W, Garhwal A (2015) Currency security and forensics: a survey. Multimed Tools Appl 74(11):4013–4043

    Article  Google Scholar 

  4. Ali Qureshi M, Deriche M (2014) A review on copy move image forgery detection techniques. In: 11th International multi-conference on systems, signals and devices (SSD). Barcelona, Spain, pp 1–5

  5. Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10(3):226–245

    Article  Google Scholar 

  6. Al-Qershi OM, Khoo BE (2013) Passive detection of copy–move forgery in digital images: state-of-the-art. Forensic Sci Int 231(1–3):284–295

    Article  Google Scholar 

  7. Kakar P, Sudha N (2012) Exposing postprocessed copy–paste forgeries through transform-invariant features. IEEE Trans Inf Forensics Secur 7(3):1018–1028

    Article  Google Scholar 

  8. Qazi T, Hayat K, Khan SU (2013) Survey on blind image forgery detection. IET Image Process 7(7):660–670

    Article  Google Scholar 

  9. Pun C, Yuan X, Bi X (2015) Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Trans Inf Forensics Secur 10(8):1705–1716

    Article  Google Scholar 

  10. Bravo-Solorio S, Nandi AK (2011) Exposing duplicated regions affected by reflection, rotation and scaling. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). Prague, pp 1880–1883

  11. 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):1355–1370

    Article  Google Scholar 

  12. Davarzani R, Yaghmaie K, Mozaffari S (2013) Copy–move forgery detection using multiresolution local binary patterns. Forensic Sci Int 231(1–3):61–72

    Article  Google Scholar 

  13. Cozzolino D, Poggi G, Verdoliva L (2014) Copy–move forgery detection based on patchmatch. In: 2014 IEEE international conference on image processing (ICIP). Paris, France, pp 5312–5316

  14. Fattah SA, Ullah MMI, Ahmed M (2014) A scheme for copy–move forgery detection in digital images based on 2D-DWT. In: IEEE 57th international midwest symposium on circuits and systems (MWSCAS). College Station, TX, pp 801–804

  15. Imamoglu M, Ulutas G, Ulutas M (2013) Detection of copy–move forgery using Krawtchouk moment. In: 2013 8th International conference on electrical and electronics engineering. Bursa, Turkey, pp 311–314

  16. Lee J, Chang C, Chen W (2015) Detection of copy–move image forgery using histogram of orientated gradients. Inf Sci 321:250–262

    Article  Google Scholar 

  17. 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):1–12

    Google Scholar 

  18. 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):158–166

    Google Scholar 

  19. Ketenci S, Ulutas G, Ulutas M (2014) Detection of duplicated regions in images using 1D-Fourier transform. In: International conference on systems, signals and image processing. Dubrovnik, Croatia, pp 171–174

  20. Lee JC (2015) Copy–move image forgery detection based on Gabor magnitude. J Vis Commun Image Represent 31:320–334

    Article  Google Scholar 

  21. Muhammad G, Hussain M, Bebis G (2012) Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit Investig 9(1):49–57

    Article  Google Scholar 

  22. Al-Qershi OM, Khoo BE (2015) Enhanced matching method for copy–move forgery detection by means of Zernike moments. In: 13th International workshop on digital-forensics and watermarking (IWDW 2014), LNCS 9023, pp 485–497

  23. 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):1099–1110

    Article  Google Scholar 

  24. 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 29:16–32

    Article  Google Scholar 

  25. Kakar P, Sudha N (2012) Exposing postprocessed copy–paste forgeries through transform-invariant features. IEEE Trans Inf Forensics Secur 7(3):1018–1028

    Article  Google Scholar 

  26. Caldelli R, Amerini I, Ballan L (2012) On the effectiveness of local warping against SIFT-based copy–move detection. In: Proceedings of the 5th international symposium on communications, control and signal processing. Rome, Italy, pp 1–5

  27. Ardizzone E, Bruno A, Mazzola G (2015) Copy–move forgery detection by matching triangles of keypoints. IEEE Trans Inf Forensics Secur 10(10):2084–2094

    Article  Google Scholar 

  28. Costanzo A, Amerini I, Caldelli R (2014) Forensic analysis of SIFT keypoint removal and injection. IEEE Trans Inf Forensics Secur 9(9):1450–1464

    Article  Google Scholar 

  29. Li J, Li X, Yang B (2015) Segmentation-based image copy–move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518

    Article  MathSciNet  Google Scholar 

  30. 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):244–254

    Article  Google Scholar 

  31. Jaberi M, Bebis G, Hussain M (2014) Accurate and robust localization of duplicated region in copy–move image forgery. Mach Vis Appl 25(2):451–475

    Article  Google Scholar 

  32. Yu L, Han Q, Niu X (2015) Feature point-based copy–move forgery detection: covering the non-textured areas. Multimed Tools Appl. doi:10.1007/s11042-014-2362-y

    Google Scholar 

  33. Jiang YJ (2011) Exponent moments and its application in pattern recognition. Beijing University of Posts and Telecommunications, Beijing

    Google Scholar 

  34. Xiang-yang Wang, Pan-pan Niu, Hong-ying Yang, Chun-peng Wang, Ai-long Wang (2014) A new robust color image watermarking using local quaternion exponent moments. Inf Sci 277:731–754

    Article  Google Scholar 

  35. Zong T, Xiang Y, Natgunanathan I (2015) Robust histogram shape-based method for image watermarking. IEEE Trans Circuits Syst Video Technol 25(5):717–729

    Article  Google Scholar 

  36. Zhang R, Wei F, Li B (2014) E2LSH based multiple kernel approach for object detection. Neurocomputing 124(2):105–110

    Article  Google Scholar 

  37. 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

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, Liaoning, People’s Republic of China

    Xiang-yang Wang, Yu-nan Liu, Huan Xu, Pei Wang & Hong-ying Yang

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  1. Xiang-yang Wang
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  2. Yu-nan Liu
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  3. Huan Xu
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  4. Pei Wang
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  5. Hong-ying Yang
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Correspondence to Xiang-yang Wang or Hong-ying Yang.

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Wang, Xy., Liu, Yn., Xu, H. et al. Robust copy–move forgery detection using quaternion exponent moments. Pattern Anal Applic 21, 451–467 (2018). https://doi.org/10.1007/s10044-016-0588-1

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  • DOI: https://doi.org/10.1007/s10044-016-0588-1

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