Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Forensic Decision of Median Filtering Image Using a Coefficient of Variation of Fourier Transform

Fourier 변환 변이계수를 이용한 미디언 필터링 영상의 포렌식 판정

  • RHEE, Kang Hyeon (Chosun University, Dept. of Electronics Eng. /School of Design and Creative Eng)
  • 이강현 (조선대학교 전자공학과/창의공학디자인융합학과)
  • Received : 2015.05.25
  • Accepted : 2015.07.24
  • Published : 2015.08.25
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Abstract

In a distribution of digital image, there is a serious problem that is the image alteration by a forger. For the problem solution, this paper proposes the forensic decision algorithm of a median filtering (MF) image using the feature vector based on a coefficient of variation (c.v.) of Fourier transform. In the proposed algorithm, we compute Fourier transform (FT) coefficients of row and column line respectively of an image first, then c.v. between neighboring lines is computed. Subsquently, 10 Dim. feature vector is defined for the MF detection. On the experiment of MF detection, the proposed scheme is compared to MFR (Median Filter Residual) and Rhee's MF detection schemes that have the same 10 Dim. feature vector both. As a result, the performance is excellent at Unaltered, JPEG (QF=90), Down scaling (0.9) and Up scaling (1.1) images, and it showed good performance at Gaussian filtering ($3{\times}3$) image. However, in the performance evaluation of all measured items of the proposed scheme, AUC (Area Under ROC (Receiver Operating Characteristic) Curve) by the sensitivity and 1-specificity approached to 1 thus, it is confirmed that the grade of the performance evaluation is rated as 'Excellent (A)'.

디지털 영상의 배포에서, 위 변조자에 의해 영상이 변조되는 심각한 문제가 있다. 이러한 문제를 해결하기 위하여, 본 논문에서는 영상의 Fourier 변환 변이계수를 이용한 미디언 필터링 (Median Filtering: MF) 영상의 포렌식 판정 알고리즘을 제안한다. 제안된 알고리즘에서, 영상의 각 수평, 수직라인의 Fourier 변환 (Fourier Transform: FT)을 하고, 이웃 라인과의 변이계수를 기반으로 하여 MF 검출 (Median Filtering Detection: MFD)을 위한 10 Dim. 특징벡터를 정의한다. 이는 MF 검출기의 SVM (Support Vector Machine) 학습에 사용된다. 제안된 미디언 필터링 검출 스킴은 동일 10 Dim. 특징벡터의 MFR (Median Filter Residual)과 Rhee의 MF 검출 스킴과 비교하여 원영상, JPEG (QF=90), Down 스케일링 (0.9) 그리고 Up 스케일링 (1.1) 영상에서는 성능이 우수하며, Gaussian 필터링($3{\times}3$) 영상에서는 성능이 일부 높았다. 제안된 알고리즘은 성능평가 전체항목에서 민감도 (Sensitivity; TP: True Positive rate)와 1-특이도 (1-Specificity; FP: False Positive rate)에 의한 AUC (Area Under ROC (Receiver Operating Characteristic) Curve)가 모두 1에 수렴하여 'Excellent (A)' 등급임을 확인하였다.

Keywords

References

  1. Kang Hyeon RHEE, "Median Filtering Detection using Latent Growth Modeling," THE INSTITUTE OF ELECTRONICS AND INFORMATION ENGINEERS, Journal of The Institute of Electronics and Information Engineers, Vol. 52, No. 1, pp. 61-68, 2015.1. https://doi.org/10.5573/ieie.2015.52.1.061
  2. Kang Hyeon RHEE, "Image Forensic Decision Algorithm using Edge Energy Information of Forgery Image," THE INSTITUTE OF ELECTRONICS AND INFORMATION ENGINEERS, Journal of The Institute of Electronics and Information Engineers, Vol. 51, No. 3, pp. 75-81, 2014.3. https://doi.org/10.5573/ieie.2014.51.3.075
  3. Kang Hyeon RHEE, "Forensic Decision of Median Filtering by Pixel Value's Gradients of Digital Image," THE INSTITUTE OF ELECTRONICS AND INFORMATION ENGINEERS, Journal of The Institute of Electronics and Information Engineers, Vol. 52, No. 6, pp. 79-84, 2015.5. https://doi.org/10.5573/ieie.2015.52.6.079
  4. Xiangui Kang, Matthew C. Stamm, Anjie Peng, and K. J. Ray Liu, "Robust Median Filtering Forensics Using an Autoregressive Model," IEEE Trans. on Information Forensics and Security, vol. 8, no. 9, pp. 1456-1468, Sept. 2013. https://doi.org/10.1109/TIFS.2013.2273394
  5. Chenglong Chen, Jiangqun Ni and Jiwu Huang, "Blind Detection of Median Filtering in Digital Images: A Difference Domain Based Approach," Image Processing, IEEE Transactions on, Vol. 22, pp. 4699-4710, 2013. https://doi.org/10.1109/TIP.2013.2277814
  6. H. Yuan, "Blind forensics of edianfiltering in digital images," IEEE Trans. Inf. Forensics Security, vol. 6, no. 4, pp. 1335-1345, Dec. 2011. https://doi.org/10.1109/TIFS.2011.2161761
  7. Tomas Pevny, "Steganalysis by Subtractive Pixel Adjacency Matrix," Information Forensics and Security, IEEE Transactions on, Vol. 5, pp. 215-224, 2010. https://doi.org/10.1109/TIFS.2010.2045842
  8. Yujin Zhang, Shenghong Li, Shilin Wang and Yun Qing Shi, "Revealing the Traces of Median Filtering Using High-Order Local Ternary Patterns," Signal Processing Letters, IEEE, Vol. 21, pp. 275-279, 2014. https://doi.org/10.1109/LSP.2013.2295858
  9. http://bows2.ec-lille.fr/ (2015.4.22)
  10. Kang Hyeon RHEE, "Framework of multimedia forensic system," Computing and Convergence Technology (ICCCT), 2012 7th International Conferenceon, IEEE Conf. Pub., pp.1084-1087, 2012.