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Detecting filtered cloning in digital images
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International Multimedia Conference archive
Proceedings of the 9th workshop on Multimedia & security table of contents
Dallas, Texas, USA
SESSION: Authentication and forensics table of contents
Pages: 43 - 50  
Year of Publication: 2007
ISBN:978-1-59593-857-2
Authors
Brandon Dybala  Saint Louis University, St. Louis, MO
Brian Jennings  Saint Louis University, St. Louis, MO
David Letscher  Saint Louis University, St. Louis, MO
Sponsors
ACM: Association for Computing Machinery
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
Publisher
ACM  New York, NY, USA
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ABSTRACT

We present an efficient technique to detect portions of a digital image that have been modified using textural information from another region of the image, for example Photoshop's healing brush or Poisson cloning. Common uses of these tools include removing damaged areas, blemishes and sometimes larger objects from a scene. We show that the methods are efficient and accurately identify the use of a large class of image manipulation techniques for uncompressed TIFF images and high quality compressed images. When applied to images that have higher compression levels accurate results are also obtained if the region that has been duplicated is sufficiently large.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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H. Farid and E. Simoncelli. Differentiation of discrete multi-dimensional signals. IEEE Transactions on Image Processing, 13(4):496--508, 2004.
 
3
J. Fridrich, J. Lukas, and M. Goljan. Detecting digital image forgeries using sensor pattern noise. In Proc. of SPIE Electronic Imaging, 2006.
 
4
J. Fridrich, D. Soukal, and J. Lukas. Detection of copy-move forgery in digital images. In Proc. of DFRWS, 2003.
 
5
T. Georgiev. Vision, healing brush, and fiber bundles. In Proceedings of SPIE, 2005.
6
7
 
8
S. C. Johnson. Hierarchical clustering schems. Psychometricka, 2:241--254, 1967.
 
9
H. Pearson. Image manipulation csi: Cell biology. Nature, 434:952--953, 2005.
10
 
11
A. Popescu and H. Farid. Exposing digital forgeries by detecting duplicated image regions. Technical Report TR2004-515, Department of Computer Science, Dartmouth College, 2004.
 
12
A. Popescu and H. Farid. Exposing digital forgeries by detecting traces of re-sampling. IEEE Transactions on Signal Processing, 53(2):758--767, 2005.

Collaborative Colleagues:
Brandon Dybala: colleagues
Brian Jennings: colleagues
David Letscher: colleagues