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Quantization-based Markov feature extraction method for image splicing detection

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Abstract

In this paper, we propose an efficient Markov feature extraction method for image splicing detection using discrete cosine transform coefficient quantization. The quantization operation reduces the information loss caused by the coefficient thresholding used to restrict the number of Markov features. The splicing detection performance is improved because the quantization method enlarges the discrimination of the probability distributions between the authentic and the spliced images. In this paper, we present two Markov feature selection algorithms. After quantization operation, we choose the sum of three directional Markov transition probability values at the corresponding position in the probability matrix as a first feature vector. For the second feature vector, the maximum value among the three directional difference values of the three color channels is used. A fixed number of features, regardless of the color channels and test datasets, are used in the proposed algorithm. Through experimental simulations, we demonstrate that the proposed method achieves high performance in splicing detection. The average detection accuracy is over than 97% on three well-known splicing detection image datasets without the use of additional feature reduction algorithms. Furthermore, we achieve reasonable forgery detection performance for more modern and realistic dataset.

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References

  1. Farid, H.: A picture tells a thousand lies. New Sci. 2411, 38–41 (2003)

    Google Scholar 

  2. Farid, H.: A survey of image forgery detection. IEEE Signal Process. Mag. 26, 6–25 (2009)

    Article  Google Scholar 

  3. Mahdian, B., Saic, S.: A bibliography on blind methods for identifying image forgery. Signal Process. Image Commun. 25(6), 389–399 (2010)

    Article  Google Scholar 

  4. Ng, T.T., Chang, S.F., Sun, Q.: Blind detection of photomontage using higher order statistics. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), pp. 688–691 (2004)

  5. Ng, T.T.; Chang, S.F.: A model for image splicing. In: IEEE International Conference on Image Processing, Singapore, pp. 1169–1172 (2004)

  6. Ng, T.T., Chang, S.F.: A dataset of authentic and spliced image blocks. Technical Report 203–2004, Columbia University (2004). http://www.ee.columbia.edu/ln/dvmm/downloads/

  7. 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: Digital Watermarking, Proceedings of the 5th International Workshop on Digital Watermarking (IWDW), 4283, pp. 177–187 (2006)

  8. Chen, W., Shi, Y. Q., Su, W.: Image splicing detection using 2-D phase congruency and statistical moments of characteristic function. In: SPIE Electronic Imaging: Security, Steganography, and Watermarking of Multimedia Contents, pp. 65050R.1–65050R.8 (2007)

  9. Shi, Y.Q., Chen, C., Chen, W.: A natural image model approach to splicing detection. In: Proceedings of ACM Multimedia and Security (MM&Sec), pp. 51–62 (2007)

  10. Dong, J., Wang, W., Tan, T., Shi, Y.Q.: Run-length and edge statistics based approach for image splicing detection. In: Proceedings of the 7th International Workshop on Digital Watermarking, 5450, pp. 76–87 (2009)

  11. He, Z., Sun, W., Lu, W., Lu, H.: Digital image splicing detection based on approximate run length. Pattern Recognit. Lett. 32(12), 591–1597 (2011)

    Article  Google Scholar 

  12. He, Z., Lu, W., Sun, W.: Improved run length based detection of digital image splicing. In: Proceedings of the 10th International Workshop on Digital-Forensics and Watermarking (IWDW), pp. 349–360 (2012)

  13. Muhammad, G., Al-Hammadi, M.H.: Image forgery detection using steerable pyramid transform and local binary pattern. Mach. Vis. Appl. 25(4), 985–995 (2014)

    Article  Google Scholar 

  14. Dong, J., Wang, W.: CASIA tampered image detection evaluation (TIDE) database, v1.0 and v2.0 (2011). http://forensics.idealtest.org/

  15. He, Z., Lu, W., Sun, W., Huang, J.: Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recognit. 45(12), 4292–4299 (2012)

    Article  Google Scholar 

  16. Su, B., Yuan, Q., Wang, S., Zhao, C., Li, S.: Enhanced state selection Markov model for image splicing detection. EURASIP J. Wirel. Commun. 2014(7), 1–10 (2014)

    Google Scholar 

  17. El-Alfy, M., Qureshi, M. A.: Combining spatial and DCT based Markov features for enhanced blind detection of image splicing. Pattern Anal. Appl., pp. 1–11 (2014)

  18. Zhao, X., Wang, S., Li, S., Li, J.: Passive image-splicing detection by a 2-D noncausal Markov model. IEEE Trans. Circuits Syst. Video Technol. 25(2), 185–199 (2015)

    Article  Google Scholar 

  19. Zhang, J., Zhao, Y., Su, Y.: A new approach merging Markov and DCT features for image splicing detection. Proc. IEEE Int. Conf. Intell. Comput. Intell. Syst. 4, 390–394 (2009)

    Google Scholar 

  20. Li, C., Ma, Q., Xiao, L., Li, M., Zhang, A.: Image splicing detection based on Markov features in QDCT domain. In: Huang, D. H., Han, K. (eds.) ICIC 2015, Part III. LNCS. 9226, pp. 170–176 (2015)

  21. Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhao, X., Li, S., Wang, S., Li, J., Yang, K.: Optimal chromalike channel design for passive image splicing detection. EURASIP J. Adv. Signal Process. 240, 2012 (2012). https://doi.org/10.1186/1687-6180-2012-240

    Google Scholar 

  23. Moghaddasi, Z., Jalab, H.A., Md Noor, R.: Improving RLRN image splicing detection with the use of PCA and Kernel PCA. Sci. World J. Article ID 606570 (2014). https://doi.org/10.1155/2014/606570

  24. Chang, C.C.; Lin, C.J.: Libsvm-A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, (2011). https://doi.org/10.1145/1961189.1961199

  25. http://ifc.recod.ic.unicamp.br/fc.website/index.py

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant Number: 2012R1A1A2042034).

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Authors and Affiliations

  1. Department of Electronics Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, 46241, South Korea

    Jong Goo Han & Il Kyu Eom

  2. Department of Mechatronics Engineering, TongMyoung University, 428, Sinseon-ro, Nam-gu, Busan, 48520, South Korea

    Tae Hee Park

  3. Department of Aerospace and Software Engineering, 501 Jinjudaro, Jinju, 52828, South Korea

    Yong Ho Moon

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  1. Jong Goo Han
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  2. Tae Hee Park
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  3. Yong Ho Moon
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Correspondence to Il Kyu Eom.

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Han, J.G., Park, T.H., Moon, Y.H. et al. Quantization-based Markov feature extraction method for image splicing detection. Machine Vision and Applications 29, 543–552 (2018). https://doi.org/10.1007/s00138-018-0911-5

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  • DOI: https://doi.org/10.1007/s00138-018-0911-5

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