skip to main content
10.1145/1877972.1877990acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
poster

Copy-move forgery detection via texture description

Published:29 October 2010Publication History

ABSTRACT

Copy-move forgery is one of the most common type of tampering in digital images. Copy-moves are parts of the image that are copied and pasted onto another part of the same image. Detection methods in general use block-matching methods, which first divide the image into overlapping blocks and then extract features from each block, assuming similar blocks will yield similar features. In this paper we present a block-based approach which exploits texture as feature to be extracted from blocks. Our goal is to study if texture is well suited for the specific application, and to compare performance of several texture descriptors. Tests have been made on both uncompressed and JPEG compressed images.

References

  1. Sencar, T., Memon N. 2008, Overview of State-of-the-art in Digital Image Forensics. World Scientific PressGoogle ScholarGoogle Scholar
  2. Farid H. 2009, A Survey of Image Forgery Detection, IEEE Signal Processing Magazine, 26(2):16--25Google ScholarGoogle ScholarCross RefCross Ref
  3. Bayram, S., Sencar, T., and Memon N. 2008, A Survey of Copy-Move Forgery Detection Techniques, IEEE Western New York Image Processing Workshop, Sept. 2008, NYGoogle ScholarGoogle Scholar
  4. Fridrich, J., Soukal, D., and Luk, J., 2003, Detection of Copymove Forgery in Digital Images, Proc. Digital Forensic Research Workshop, Cleveland, OH, August 2003.Google ScholarGoogle Scholar
  5. Popescu, A.C. and Farid, H. 2004, Exposing Digital Forgeries by Detecting Duplicated Image Regions, Technical Report, TR2004--515, Dartmouth College, Computer Science.Google ScholarGoogle Scholar
  6. Li, G., Wu, Q., Dan Tu and Sun S., 2007, A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD, Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007, 1750--1753.Google ScholarGoogle Scholar
  7. Luo, W., Huang, J., and Qiu, G. 2006. Robust Detection of Region-Duplication Forgery in Digital Image. In Proceedings of the 18th international Conference on Pattern Recognition - Volume 04, 746--749. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. T. Sikora, 2001, The MPEG-7 visual standard for content description-an overview, IEEE Trans. Circuits Syst. Video Technol. 11 (6), pp. 696--702. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Feng Xu, Yu-Jin Zhang, 2006, Evaluation and comparison of texture descriptors proposed in MPEG-7, Journal of Visual Communication and Image Representation, Volume 17, Issue 4, August 2006, Pages 701--716Google ScholarGoogle Scholar
  10. Howarth P. and Ruger S., 2004), Evaluation of Texture Features for Content-Based Image Retrieval, Proc. of CIVR 2004, LNCS 3315, pp. 326--334Google ScholarGoogle Scholar
  11. Won, C.S., Park, D.K., 2002, Efficient Use of MPEG-7 Edge Histogram Descriptor. ETRI. Journal 24, 23--30.Google ScholarGoogle Scholar
  12. H. Tamura, S. Mori, T. Yamawaki, Texture features corresponding to visual perception, IEEE Trans. Syst. Man Cybern. 8 (6) (1978) 460--473.Google ScholarGoogle Scholar
  13. Jain A.K. and Farrokhia F. 1991, Unsupervised Texture Segmentation Using Gabor Filters, Pattern Recognition, Vol. 24, No. 12, pp. 1167--86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Robert M. Haralick, 1979, Statistical and structural approaches to texture, Proc. IEEE, vol. 67, no. 5, pp. 786--804.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Copy-move forgery detection via texture description

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      MiFor '10: Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
      October 2010
      134 pages
      ISBN:9781450301572
      DOI:10.1145/1877972

      Copyright © 2010 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 29 October 2010

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • poster

      Upcoming Conference

      MM '24
      MM '24: The 32nd ACM International Conference on Multimedia
      October 28 - November 1, 2024
      Melbourne , VIC , Australia

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader