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Selective Feature Extraction Method Between Markov Transition Probability and Co-occurrence Probability for Image Splicing Detection
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 Title & Authors
Selective Feature Extraction Method Between Markov Transition Probability and Co-occurrence Probability for Image Splicing Detection
Han, Jong-Goo; Eom, Il-Kyu; Moon, Yong-Ho; Ha, Seok-Wun;
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 Abstract
In this paper, we propose a selective feature extraction algorithm between Markov transition probability and co-occurrence probability for an effective image splicing detection. The Features used in our method are composed of the difference values between DCT coefficients in the adjacent blocks and the value of Kullback-Leibler divergence(KLD) is calculated to evaluate the differences between the distribution of original image features and spliced image features. KLD value is an efficient measure for selecting Markov feature or Co-occurrence feature because KLD shows non-similarity of the two distributions. After training the extracted feature vectors using the SVM classifier, we determine whether the presence of the image splicing forgery. To verify our algorithm we used grid search and 6-folds cross-validation. Based on the experimental results it shows that the proposed method has good detection performance with a limited number of features compared to conventional methods.
 Keywords
DCT;Markov feature;Co-occurrence feature;Image forgery;Image splicing;SVM;
 Language
Korean
 Cited by
 References
1.
J. Dong, W. Wang, T. Tan, and Y. Q. Shi, "Run-Length and edge statistics based approach for image splicing detection," LNCS, vol. 5450, pp. 76-87, Mar. 2009.

2.
Z. He, W. Lu, and W, Sun, "Improved run length based detection of digital image splicing," LNCS, vol. 7218, pp. 349-360, Jan. 2012.

3.
H. Farid, "A survey of image forgery detection," IEEE Signal Processing Magazine, vol. 26, no. 2, pp. 16-25, Sep. 2009. crossref(new window)

4.
T. T. Ng, S. F. Chang and Q. Sun, "Blind detection of photomontage using higher order statistics", Proceedings of IEEE International Symposium on Circuits and Systems, vol. 5, pp. 688-691, May. 2004.

5.
D. Fu, Y. Q. Shi, and W. Su, "Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition," LNCS, vol. 4283, pp. 177-187, Feb. 2006.

6.
W. Chen, Y. Q. Shi, and W. Su, "Image splicing detection using 2-D phase congruency and statistical moments of characteristic function," Proceedings of SPIE Electronic Imaging: Security, Steganography, and Watermarking of Multimedia Contents, vol. 6505, pp. 1-8, Jul. 2007.

7.
Z He, W Lu, W Sun, J Huang, "Digital image splicing detection based on Markov features in DCT and DWT domain," Pattern Recognition, vol. 45, no. 12, pp. 4292 - 4299, Dec. 2012. crossref(new window)

8.
B. Su, Q. Yuan, S. Wang, C. Zhao and S. Li, "Enhanced state selection Markov model for image splicing detection," EURASIP Journal on Wireless Communications and Networking, vol. 2014, no. 1, pp. 1-10, Dec. 2014. crossref(new window)

9.
S. Kullback and R. A. Leibler, "On information and sufficiency," The Annals of Mathematical Statistics, vol. 22, no. 1, pp. 79-86. Mar. 1951. crossref(new window)

10.
G. Muhammad, and M. H. Al-Hammadi, "Image forgery detection using steerable pyramid transform and local binary pattern," Machine Vision and Applications, vol. 25, no. 4, pp. 985-995, May. 2014. crossref(new window)

11.
Columbia DVMM Research Lab. Columbia image splicing detection evaluation dataset[Internet], Available: http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/AuthSplicedDataSet.htm.

12.
J. Dong, and W. Wang, CASIA tampered image detection evaluation (TIDE) database[Internet], Available: CASIA v1.0 and v2.0, http://forensics.idealtest.org/.