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Can we trust digital image forensics?
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International Multimedia Conference archive
Proceedings of the 15th international conference on Multimedia table of contents
Augsburg, Germany
SESSION: Content 1 - content analysis applications table of contents
Pages: 78 - 86  
Year of Publication: 2007
ISBN:978-1-59593-702-5
Authors
Thomas Gloe  Technische Universität Dresden, Dresden, Germany
Matthias Kirchner  Technische Universität Dresden, Dresden, Germany
Antje Winkler  Technische Universität Dresden, Dresden, Germany
Rainer Böhme  Technische Universität Dresden, Dresden, Germany
Sponsors
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
Publisher
ACM  New York, NY, USA
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ABSTRACT

Compared to the prominent role digital images play in nowadays multimedia society, research in the field of image authenticity is still in its infancy. Only recently, research on digital image forensics has gained attention by addressing tamper detection and image source identification. However, most publications in this emerging field still lack rigorous discussions of robustness against strategic counterfeiters, who anticipate the existence of forensic techniques. As a result, the question of trustworthiness of digital image forensics arises. This work will take a closer look at two state-of-the-art forensic methods and proposes two counter-techniques; one to perform resampling operations undetectably and another one to forge traces of image origin. Implications for future image forensic systems will be discussed.


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.

 
1
W. Chen, Y. Q. Shi, and W. Su. Image splicing detection using 2-D phase congruency and statistical moments of characteristic function. In E. J. Delp and P. W. Wong, editors, Proc. of SPIE: Security, Steganography, and Watermarking of Multimedia Contents IX, volume 6072, pages 65050R-1--65050R-8, 2007.
 
2
K. S. Choi, E. Y. Lam, and K. Y. Wong. Automatic source camera identification using the intrinsic lens radial distortion. In Optics Express, volume 14, pages 11551--11565, Nov. 2006.
 
3
A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1):1--38, 1977.
 
4
Z. J. Geradts, J. Bijhold, M. Kieft, K. Kurosawa, K. Kuroki, and N. Saitoh. Methods for identification of images acquired with digital cameras. In S. K. Bramblea, E. M. Carapezza, and L. I. Rudin, editors, Proc. of SPIE: Enabling Technologies for Law Enforcement and Security, volume 4232, pages 505--512, 2001.
 
5
T. Gloe, E. Franz, and A. Winkler. Forensics for flatbed scanners. In E. J. Delp and P. W. Wong, editors, Proc. of SPIE: Security, Steganography, and Watermarking of Multimedia Contents IX, volume 6072, pages 65051I-1--65051I-12, 2007.
6
 
7
M. Kharrazi, H. T. Sencar, and N. Memon. Blind source camera identification. In International Conference on Image Processing, volume 1, pages 709--712, 2004.
 
8
M. Kirchner and R. Böhme. Tamper hiding: Defeating image forensics. In Information Hiding, Ninth International Workshop, St. Malo, France, June 11-13 2007, to appear in LNCS.
 
9
 
10
J. Lukàš and J. Fridrich. Estimation of primary quantization matrix in double compressed JPEG images. In Proc. of the Digital Forensic Research Workshop, 2003.
 
11
J. Lukàš, J. Fridrich, and M. Goljan. Detecting digital image forgeries using sensor pattern noise. In A. Said and J. G. Apostolopoulos, editors, Proc. of SPIE: Image and Video Communications and Processing 2005, volume 5685, pages 249--260, 2005.
 
12
J. Lukàš, J. Fridrich, and M. Goljan. Digital camera identification from sensor noise. IEEE Transactions on Information Forensics and Security, 1(2):205--214, 2006.
 
13
M. K. Mih#&231;ak, I. Kozintsev, K. Ramchandran, and P. Moulin. Low-complexity image denoising based on statistical modeling of wavelet coefficients. IEEE Signal Processing Letters, 6(12):300--303, Dec. 1999.
 
14
T.-T. Ng, S.-F. Chang, C.-Y. Lin, and Q. Sun. Passive-blind image forensics. In W. Zeng, H. Yu, and C.-Y. Lin, editors, Multimedia Security Technologies for Digital Rights. Academic Press, 2006.
 
15
 
16
A. Popescu and H. Farid. Exposing digital forgeries by detecting traces of re-sampling. IEEE Trans. on Signal Processing, 53(2):758--767, 2005.
 
17
A. Popescu and H. Farid. Exposing digital forgeries in color filter array interpolated images. IEEE Trans. on Signal Processing, 53(10):3948--3959, 2005.
 
18
 
19
M. Vrhel, E. Saber, and H. J. Trussell. Color image generation and display technologies. IEEE Signal Processing Magazine, pages 23--33, Jan. 2005.
 
20

Collaborative Colleagues:
Thomas Gloe: colleagues
Matthias Kirchner: colleagues
Antje Winkler: colleagues
Rainer Böhme: colleagues