한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제15권11호
  • /
  • Pages.1292-1304
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  • 2012
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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수정된 디지털 이미지에서 기하학적 변형 및 필터링 변형 영역을 식별하는 기법

Identification Method of Geometric and Filtering Change Regions in Modified Digital Images

  • 황민구 (중앙대학교 첨단영상대학원) ;
  • 조병주 (중앙대학교 첨단영상대학원) ;
  • 하동환 (중앙대학교 첨단영상대학원)
  • 투고 : 2012.08.08
  • 심사 : 2012.10.17
  • 발행 : 2012.11.30
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초록

최근에 디지털 이미지는 광고주나 이미지 편집자들에 의해 극단적으로 수정되는 경우가 많다. 또한 일반인들도 간단한 편집 프로그램을 통해 원본 이미지를 수정함으로써 사진을 조작하곤 한다. 본 논문에서는 이러한 문제를 해결하기 위해서 수정된 이미지에 대한 식별 및 분석 방법을 제안하고자 한다. 수정 이미지 분석에는 크게 기하학적 변형(geometric changes)과 필터링 변형(filtering change)으로 나누었다. 이러한 변형에는 항상 보간법을 기반으로 한 알고리즘을 내포하고 있기 때문에 보간의 흔적을 분석할 수 있는 알고리즘을 제안하였다. 그 방법으로 minimum 필터와 laplacian 연산 그리고 maximum 필터를 이용한 보간 검출 맵을 구현하였으며, 실제로 수정된 이미지에 제안하는 알고리즘을 적용하여 변형된 흔적을 검출 맵을 통해 분석할 수 있었다.

Recently, digital images are extremely forged by editors or advertisers. Also, amateurs can modify images throughout easy editing programs. In this study, we propose identification and analytical methods for the modified images to figure out those problems. In modified image analysis, we classify two parts; a filtering change and a geometric change. Those changes have an algorithm based on interpolation so that we propose the algorithm which is able to analyze a trace on a modified area. With this algorithm, we implement a detection map of interpolation using minimum filter, laplacian algorithm, and maximum filter. We apply the proposed algorithm to modified image and are able to analyze its modified trace using the detection map.

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참고문헌

  1. N. Philip, Jr. Myers, and Biocca. FA, "The Effect of Television Advertising and Programming on Body Image Distortions in Young Women," Journal of Communication, Vol. 42, No. 3, pp. 108-133, 1992. https://doi.org/10.1111/j.1460-2466.1992.tb00802.x
  2. H. Lavine, D. Sweeney, and SH. Wagner, "Depicting Women as Sex Objects in Television Advertising: Effects on Body Dissatisfaction," Personality and Social Psychology Bulletin, Vol. 25, No. 8, pp. 1049-1058, 1999. https://doi.org/10.1177/01461672992511012
  3. A. Daniel and TD. Stacey, "The Impact of Media Exposure on Males' Body Image," Journal of Social and Clinical Psychology, Vol. 23, No. 23, pp. 7-22, 2004. https://doi.org/10.1521/jscp.23.1.7.26988
  4. H. Dittmar, "How Do Body Perfect Ideals in the Media Have a Negative Impact on Body Image and Behaviors?: Factor and Processes Related to Self and Identity," Journal of Social and Clinical Psychology, Vol. 28, No. 1, pp. 1-8, 2009. https://doi.org/10.1521/jscp.2009.28.1.1
  5. D. Smeesters, T. Mussweiler, and N. Mandel, "The Effects of Thin and Heavy Media Images on Overweight and Underweight Consumers: Social Comparison Processes and Behavioral Implications," Journal of Consumer Research, Vol. 36, No. 6, pp. 930-949, 2010. https://doi.org/10.1086/648688
  6. E. Kee and H. Farid, "A Perceptual Metric for Photo Retouching," Proc. of the National Academy of Sciences of United States of America, Vol. 108, No. 50, pp. 19907-19912, 2011. https://doi.org/10.1073/pnas.1110747108
  7. A.C. Popescu and H. Farid, "Exposing Digital Forgeries by Detecting Traces of Re-sampling," IEEE Transactions on Signal Processing, Vol. 53, No. 2, pp. 758-767, 2005. https://doi.org/10.1109/TSP.2004.839932
  8. H. Farid, "Exposing Digital Forgeries in Color Filter Array Interpolated Images," IEEE Transactions on Signal Processing, Vol. 53, No. 10, pp. 3948-3959, 2005. https://doi.org/10.1109/TSP.2005.855406
  9. M. Kirchner and T. Gloe, "On Sampling Detection in Re-compressed Images," Proc. IEEE International Workshop on Information Forensics and Security, pp. 21-25, 2009.
  10. A.C. Gallagher, "Detection of Linear and Cubic Interpolation in JPEG Compressed Images," The 2nd Canadian Conference on Computer and Robot Vision, pp. 65-72, 2005.
  11. A.C. Gallagher and T. Chen, "Image Authentication by Detecting Traces of Demosaicing," Proc. Computer Vision and Pattern Recognition Workshops, pp. 1-8, 2008.
  12. S. Prasad and K.R. Ramakrishnan, "Onesampling Detection and Its Application to Image Tampering," Proc. of the IEEE International Conference on Multimedia and Expo, pp. 1325-1328, 2006.
  13. B. Mahdian and S. Saic, "Blind Methods for Detecting Image Fakery," IEEE International Carnahan Conference on Security Technology, pp. 280-286, 2008.
  14. G.S. Song, Y.I. Yun, and W.H. Lee, "Analysis on Digital Image Composite using Interpolation," Journal of Korea Multimedia Society, Vol. 13, No. 3, pp. 457-466, 2010.
  15. M.G. Hwang and D.H. Har, "Detection of Forged Regions and Filtering Regions of Digital Images using the Characteristics of Re-interpolation," Journal of Korea Multimedia Society, Vol. 15, No. 2, pp. 179-194, 2012. https://doi.org/10.9717/kmms.2012.15.2.179