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Accurate and robust localization of duplicated region in copy–move image forgery

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Abstract

Copy–move image forgery detection has recently become a very active research topic in blind image forensics. In copy–move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy–move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy–move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using “dense” MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy–move image forgery with higher accuracy, especially when the size of the duplicated region is small.

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Notes

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    Since in finding correspondences, a higher threshold yields a lower number of matches, we define the high and low values of hysteresis thresholding in opposite order compared to their definition in the literature.

References

  1. 1.

    Luo, W., Huang, J., Qiu, G.: Robust detection of region-duplication forgery in digital images. In: Proceedings of International Conference on Pattern Recognition, pp. 746–749. Washington, D.C. (2006)

  2. 2.

    Farid, H.: A survey of image forgery detection. IEEE Signal Process. Mag. 2(26), 16–25 (2009)

  3. 3.

    Zhang, J., Feng, Z., Su, Y.: A new approach for detecting copy-move forgery in digital images. In: IEEE Singapore International Conference on Communication Systems, pp. 362–366. (2008)

  4. 4.

    Fridrich, J., Soukal, D., Lukas, J.: Detection of Copy-Move Forgery in Digital Images. Department of Electrical and Computer Engineering, Department of Computer Science SUNY Binghamton, Binghamton, NY (2003)

  5. 5.

    Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting duplicated image regions. In: Technical Report TR2004-515, Dartmouth College, August (2004)

  6. 6.

    Lin, Z., Wang, R., Tang, X., Shum, H.-V.: Detecting doctored images using camera response normality and consistency. In: Proceedings of Computer Vision and Pattern Recognition. San Diego, CA, (2005)

  7. 7.

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

  8. 8.

    Li, G., Wu, Q., Tu, D., Sun, S.: A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In: IEEE International Conference on Multimedia and Expo, pp. 1750–1753. Beijing, China (2007)

  9. 9.

    Khan, S., Kulkarni, A.: An efficient method for detection of copy-move forgery using discrete wavelet transform. Int. J. Comput. Sci. Eng. (IJCSE) 2(5), 1801–1806 (2010)

  10. 10.

    Muhammad, G., Hussain, M., Khawaji, K., Bebis, G.: Blind copy move image forgery detection using dyadic uncedimated wavelet transfor. In: 17th International Conference on Digital Signal Processing. Corfu, Greece, July (2011)

  11. 11.

    Huang, H., Guo, W., Zhang, Y.: Detection of copy-move forgery in digital images using SIFT algorithm. In: IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application (2008)

  12. 12.

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

  13. 13.

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

  14. 14.

    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)

  15. 15.

    Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proceedings of Eighth International Conference on Computer Vision, pp. 525–531. (2001)

  16. 16.

    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

  17. 17.

    Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: WLD: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell 32, 1705–1720 (2010)

  18. 18.

    Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3D objects. Int. J. Comput. Vis. 73(3), 800–807 (2007)

  19. 19.

    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition. San Diego, CA, June 20–25 (2005)

  20. 20.

    Guo, X., Cao, X., Zhang, J., Li, X.: Mift: A mirror reflection invariant feature descriptor. In: Proceedings of ACCV (2009)

  21. 21.

    Ojala, T., PietikaÈinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognit. 29, 51–59 (1996)

  22. 22.

    Chung, Y.C., Tony, H.X., He, Z.: Building recognition using sketch-based representations and spectral graph matching. In: IEEE International Conference on Computer Vision (ICCV 2009), Kyoto

  23. 23.

    Ke, Y., Sukthankar, R.: PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. CVPR, Washington, DC (2004)

  24. 24.

    Mahdian, B., Siac, S.: A bibliography on blind methods for identifying image forgery. In: Signal Processing: Image Communication, pp. 389–399. (2010)

  25. 25.

    Kumar, S., Das, P.K.: Copy-move forgery detection in digital images: progress and challenges. Int. J. Comput. Sci. Eng. 3(2), 652–663 (February 2011)

  26. 26.

    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)

  27. 27.

    Raguram, R., Frahm, M., Pollefeys, M.: A comparative analysis of RANSAC techniques leading to adaptive real-time random sample consensus. In: ECCV 2008, Part II, LNCS, vol. 5305, pp. 500–503. Springer, Heidelberg (2008)

  28. 28.

    Provost, F., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing induction algorithms. In: Proceeding of the 15th International Conference on Machine Learning, pp. 445–453. Morgan Kaufmann, San Francisco (1998)

  29. 29.

    CASIA Image Tampering Detection Evaluation Database, ver. 2.0. http://forensics.idealtest.org. (2010)

  30. 30.

    Granty, R., Aditya, T., Madhu, S.: Survey on passive methods of image tampering detection. In: 2010 International Conference on Communication and Computational Intelligence (INCOCCI), Dec., pp. 431–436. (2010)

  31. 31.

    Vedaldi, A., Fulkerson, B.: An Open and Portable Library of Computer Vision Algorithms. http://www.vlfeat.org/. (2008)

  32. 32.

    Beis, J., Lowe, D.G.: Shape indexing using approximate nearest-neighbor search in high-dimensional spaces. In: Conference on Computer Vision and Pattern Recognition, Puerto Rico, pp. 1000–1006. (1997)

  33. 33.

    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

  34. 34.

    Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(6), 765–781 (2011)

  35. 35.

    Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. In: Technical Report CVR-TR-2004-01, Beckman Institute, University of Illinois (2004)

  36. 36.

    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: European Computer Vision Conference (ECCV) (2006)

  37. 37.

    Canny, J.: A computational approach to edge detection. IEEE Trans Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

  38. 38.

    Powers, D.M.W.: Evaluation: from precision, recall and f-factor to roc, informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)

  39. 39.

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

  40. 40.

    Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)

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Acknowledgments

This work is supported by grant 10-INF1140-02 under the National Plan for Science and Technology (NPST), King Saud University, Riyadh, Saudi Arabia. George Bebis is a Visiting Professor in the Department of Computer Science at King Saud University, Saudi Arabia.

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Correspondence to George Bebis.

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Jaberi, M., Bebis, G., Hussain, M. et al. Accurate and robust localization of duplicated region in copy–move image forgery. Machine Vision and Applications 25, 451–475 (2014). https://doi.org/10.1007/s00138-013-0522-0

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Keywords

  • Blind image forensics
  • Copy–move image forgery
  • SIFT
  • MIFT
  • Matching