IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
권호 표지
DOI QR코드

DOI QR Code

Spliced Image Detection Using Characteristic Function Moments of Co-occurrence Matrix

동시 발생 행렬의 특성함수 모멘트를 이용한 접합 영상 검출

  • Received : 2015.07.22
  • Accepted : 2015.09.15
  • Published : 2015.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents an improved feature extraction method to achieve a good performance in the detection of splicing forged images. Strong edges caused by the image splicing destroy the statistical dependencies between parent and child subbands in the wavelet domain. We analyze the co-occurrence probability matrix of parent and child subbands in the wavelet domain, and calculate the statistical moments from two-dimensional characteristic function of the co-occurrence matrix. The extracted features are used as the input of SVM classifier. Experimental results show that the proposed method obtains a good performance with a small number of features compared to the existing methods.

Keywords

References

  1. B. Mahdian, S. Saic, "A bibliography on blind methods for identifying image forgery," Signal Processing: Image Communication, Vol. 25, No. 6, pp. 389-399, 2010. https://doi.org/10.1016/j.image.2010.05.003
  2. J. Redi, W. Taktak, J.L. Dugelay, "Digital image forensics: a booklet for beginners," Multimedia Tools and Applications, Vol. 51, No. 1, pp. 133-162, 2011. https://doi.org/10.1007/s11042-010-0620-1
  3. W. Wei, L. Sun, D. Tang, Y. Zhao, H. Li, "A survey of passive image forensics," Network Computing and Information Security Communications in Computer and Information Science, Vol. 345, pp. 45-55, 2012. https://doi.org/10.1007/978-3-642-35211-9_7
  4. P.S. Burvin, P.G. Scholar, J.M. Esther, "Analysis of digital image splicing detection," IOSR Journal of Computer Engineering, Vol. 16, No. 2, pp. 10-13, 2014. https://doi.org/10.9790/0661-162111013
  5. A. Rocha, W. Scheirer, T.E. Boult, S. Goldenstein, "Vision of the unseen: Current trends and challenges in digital image and video forensics," ACM Comput. Surveys, Vol. 43, No. 4, pp. 1-42, 2011.
  6. A. Kaur, R. Sharma, "Copy-Mover Forgery Detection using DCT and SIFT," International Journal of Computer Applications, Vol. 70, No. 7, pp. 30-34, 2013. https://doi.org/10.5120/11977-7847
  7. Z. Zhang, Y. Zhou, J. Kang, Y. Ren, "Study of image splicing detection, in Advanced Intelligent Computing Theories and Applications," With Aspects of Theoretical and Methodological Issues. Springer. pp.1103-1110, 2008.
  8. J. Dong, W. Wang, T. Tan, Y.Q. Shi, Run-length and edge statistics based approach for image splicing detection, Lecture Notes in Computer Science, Vol. 5450, Springer-Verlag, pp. 76-87, 2009.
  9. Q. Zheng, W. Sun, W. Lu, "Digital spliced image forensics based on edge blur measurement," Proceedings of IEEE International Conference on Information Theory and Information Security, Bejing, China, pp. 399-402, 2010.
  10. Z. He, "Digital image splicing detection based on approximate run length," Pattern Recognition Letters, Vol. 32, No. 12, pp. 1591-1597, 2011. https://doi.org/10.1016/j.patrec.2011.05.013
  11. Y.Q. Shi, C. Chen, W. Chen, "A natural image model approach to splicing detection," Proceedings of ACM Workshop on Multimedia and Security, pp. 51-62, 2007.
  12. Z. He, W. Lu, W. Sun, J. Huang, "Digital image splicing detection based on Markov features in DCT and DWT domain," Pattern Recogn, Vol. 45, No. 12, pp. 4292-4299, 2012. https://doi.org/10.1016/j.patcog.2012.05.014
  13. B. Su, Q. Yuan, S. Wang, C. Zhao, S. Li, "Enhanced state selection Markov model for image splicing detection," EURASIP Journal on Wireless Communications and Networking, Vol. 2014, pp. 1-10, 2014. https://doi.org/10.1186/1687-1499-2014-1
  14. X. Zhao S. Wang, S. Li, J. Li, "Passive Image Splicing Detection by a 2D Noncausal markov Model," IEEE Transactions on Circuits and Systems for Video Technology, Vol. 25, No. 2, pp. 185-199, 2015. https://doi.org/10.1109/TCSVT.2014.2347513
  15. T. Pevny, P. Bas, J. Fridrich, "Steganalysis by subtractive pixel adjacency matrix," IEEE Transactions on Information Forensics and Security, Vol. 5, No. 2, pp. 215-224, 2010. https://doi.org/10.1109/TIFS.2010.2045842
  16. T.T. Ng, J. Hsu, S.F. Chang. Columbia Image Splicing Detection Evaluation Dataset. Available:http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/dlform.html
  17. N. Kambhatla, T.K. Leen, "Dimension reduction by local principal component analysis," Neural Computation, Vol. 9, No. 7, pp. 1493-1516, 1997. https://doi.org/10.1162/neco.1997.9.7.1493