Abstract
Statistical feature-based pattern recognition approach has been proved to be useful in digital image forgery detection. In this paper, we first present this approach adopted in Phase 1 of the first IEEE IFS-TC Image Forensics Challenge, in which the task is to classify the tampered images from the original ones, together with the experimental results. Several different kinds of statistical features and their combinations have been tested. Finally, we have chosen to use co-occurrence matrices calculated from the rich model of noise residual images. Furthermore we have selected a subset of the rich model to further enhance the testing accuracy by about 2 % so as to reach a high detection accuracy of 93.7 %. In Phase 2 of the competition, the task is to localize the tampered regions by identifying the tampered pixels. For this purpose, we have introduced the Hamming distance of Local Binary Patterns as similarity measure to tackle the tampering without post-processing on copy-moved regions. The PatchMatch algorithm has been adopted as an efficient search algorithm for block-matching. We have also applied a simple usage of the scale-invariant feature transform (SIFT) when other kinds of processing such as rotation and scaling have been performed on the copy-moved region. The achieved f-score in identifying tampered pixels is 0.267. In summary, some success has been achieved apparently. However, much more efforts are called for to move image tampering detection ahead.
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References
Farid, H., Lyu, S.: Higher-order wavelet statistics and their application to digital forensics. In: IEEE Workshop on Statistical Analysis in Computer Vision, vol. 8, p. 94, June 2003
Fridrich, J., Soukal, D., Lukáš, J.: Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop (2003)
Johnson, K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of the 7th Workshop on Multimedia and Security, pp. 1–10. ACM, August 2005
Huang, Y.: Can digital image forgery detection be unevadable? a case study: color filter array interpolation statistical feature recovery. In: Visual Communications and Image Processing 2005, pp. 59602 W–59602 W. International Society for Optics and Photonics, July 2005
Ng, T., Chang, F., Sun, Q.: Blind detection of photomontage using higher order statistics. In: Proceedings of the 2004 International Symposium on Circuits and Systems, ISCAS 2004, vol. 5, pp. V–688. IEEE, May 2004
Fu, D., Shi, Y.Q., Su, W.: Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition. In: Shi, Y.Q., Jeon, B. (eds.) IWDW 2006. LNCS, vol. 4283, pp. 177–187. Springer, Heidelberg (2006)
Chen, W., Shi, Y.Q., Su, W.: Image splicing detection using 2-D phase congruency and statistical moments of characteristic function. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 6505, p. 26, February 2007
DVMM Research Lab. Columbia Image Splicing Detection Evaluation Dataset (2004). http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/AuthSplicedDataSet.htm
Ng, T.-T., Chang, F., Sun, Q.: Blind detection of photomontage using higher order statistics. In: Proceedings of the 2004 International Symposium on Circuits and Systems, ISCAS 2004, vol. 5, pp. V–688. IEEE, May 2004
Shi, Y.Q., Chen, C., Chen, W.: A natural image model approach to splicing detection. In: Proceedings of the 9th Workshop on Multimedia & Security, pp. 51–62. ACM, September 2007
Shi, Y.Q., Chen, C.-H., Xuan, G., Su, W.: Steganalysis versus splicing detection. In: Shi, Y.Q., Kim, H.-J., Katzenbeisser, S. (eds.) IWDW 2007. LNCS, vol. 5041, pp. 158–172. Springer, Heidelberg (2008)
He, Z., Lu, W., Sun, W., Huang, J.: Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recogn. 45(12), 4292–4299 (2012)
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)
Shi, Y.Q., Sutthiwan, P., Chen, L.: Textural features for steganalysis. In: Kirchner, M., Ghosal, D. (eds.) IH 2012. LNCS, vol. 7692, pp. 63–77. Springer, Heidelberg (2013)
Xu, G., Shi, Y.Q.: Camera model identification using local binary patterns. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 392–397. IEEE, July 2012
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm
Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Proc. 19(2), 533–544 (2010)
Sutthiwan, P., Shi, Y.Q., Zhao, H., Ng, T.-T., Su, W.: Markovian rake transform for digital image tampering detection. In: Shi, Y.Q., Emmanuel, S., Kankanhalli, M.S., Chang, S.-F., Radhakrishnan, R., Ma, F., Zhao, L. (eds.) Transactions on Data Hiding and Multimedia Security VI. LNCS, vol. 6730, pp. 1–17. Springer, Heidelberg (2011)
Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)
Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2012)
Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.: PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. -TOG 28(3), 24 (2009)
Barnes, C., Shechtman, E., Goldman, D.B., Finkelstein, A.: The generalized patchmatch correspondence algorithm. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 29–43. Springer, Heidelberg (2010)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Christlein, V., Riess, C., Jordan, J., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)
Fan, Z., de Queiroz, R.L.: Identification of bitmap compression history: JPEG detection and quantizer estimation. IEEE Trans. Image Proc. 12(2), 230–235 (2003)
Cozzolino, D., Gragnaniello, D., Verdoliva, L.: Image forgery detection through residual-based local descriptors and block-matching. In: IEEE International Conference on Image Processing (ICIP) (2014)
Cozzolino, D., Gragnaniello, D., Verdoliva, L.: Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques. In: IEEE International Conference on Image Processing (ICIP) (2014)
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Xu, G., Ye, J., Shi, YQ. (2015). New Developments in Image Tampering Detection. In: Shi, YQ., Kim, H., Pérez-González, F., Yang, CN. (eds) Digital-Forensics and Watermarking. IWDW 2014. Lecture Notes in Computer Science(), vol 9023. Springer, Cham. https://doi.org/10.1007/978-3-319-19321-2_1
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