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Selective Feature Extraction Method Between Markov Transition Probability and Co-occurrence Probability for Image Splicing Detection

접합 영상 검출을 위한 마르코프 천이 확률 및 동시발생 확률에 대한 선택적 특징 추출 방법

  • Han, Jong-Goo (Department of Electronics Engineering, Busan National University) ;
  • Eom, Il-Kyu (Department of Electronics Engineering, Busan National University) ;
  • Moon, Yong-Ho (Department of Aerospace & Software Engineering, ERI, Gyeongsang Nat. University) ;
  • Ha, Seok-Wun (Department of Aerospace & Software Engineering, ERI, Gyeongsang Nat. University)
  • Received : 2015.12.08
  • Accepted : 2016.01.19
  • Published : 2016.04.30

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.

본 논문에서는 효율적인 접합 영상 검출을 위한 마르코프 천이 및 동시발생 확률에 대한 선택적 특징 추출 방법을 제안한다. 제안하는 방법에서는 이산 코사인 변환 영역에서 블록간 계수의 차이를 이용하여 특징들을 구성하고, 특징들의 각 위치에서 원 영상과 접합영상의 특징 분포의 상이성을 확인하기 위해 Kullback-Leibler 수렴값을 구한다. 이를 바탕으로, 마르코프 확률 특징과 동시발생 확률 특징 가운데 해당 위치에서 가장 큰 차이값을 갖는 특징을 선택하여 최종 특징으로 선택하고, SVM 분류기를 이용하여 학습 및 테스트한 후 그 유효성을 판별한다. 실험 결과를 바탕으로 제안하는 방법이 기존의 방법보다 제한된 특징수로 높은 영상접합 조작 결과를 보임을 확인하였다.

Keywords

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. https://doi.org/10.1109/MSP.2008.931079
  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. https://doi.org/10.1016/j.patcog.2012.05.014
  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. https://doi.org/10.1186/1687-1499-2014-1
  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. https://doi.org/10.1214/aoms/1177729694
  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. https://doi.org/10.1007/s00138-013-0547-4
  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/.

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  1. 마코프 특징을 이용하는 고속 위조 영상 검출 알고리즘 vol.22, pp.2, 2016, https://doi.org/10.7471/ikeee.2018.22.2.227