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Abstract
Techniques for information hiding have become increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting hidden messages has become considerably more difficult. This paper describes an approach to detecting hidden messages in images that uses a wavelet-like decomposition to build higher-order statistical models of natural images. Support vector machines are then used to discriminate between untouched and adulterated images.
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Authors and Affiliations
Dartmouth College, 03755, Hanover, NH, USA
Siwei Lyu & Hany Farid
- Siwei Lyu
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- Hany Farid
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Editors and Affiliations
Microsoft Research Ltd., 7 J. J. Thomson Avenue, CB3 0FB, Cambridge, UK
Fabien A. P. Petitcolas
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© 2003 Springer-Verlag Berlin Heidelberg
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Lyu, S., Farid, H. (2003). Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines. In: Petitcolas, F.A.P. (eds) Information Hiding. IH 2002. Lecture Notes in Computer Science, vol 2578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36415-3_22
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