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Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes
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| Venue: | in Proc. Inf. Hiding Workshop, Springer LNCS |
| Citations: | 110 - 13 self |
BibTeX
@INPROCEEDINGS{Fridrich_feature-basedsteganalysis,
author = {Jessica Fridrich},
title = {Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes},
booktitle = {in Proc. Inf. Hiding Workshop, Springer LNCS},
year = {},
pages = {67--81}
}
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Abstract
Abstract. In this paper, we introduce a new feature-based steganalytic method for JPEG images and use it as a benchmark for comparing JPEG steganographic algorithms and evaluating their embedding mechanisms. The detection method is a linear classifier trained on feature vectors corresponding to cover and stego images. In contrast to previous blind approaches, the features are calculated as an L 1 norm of the difference between a specific macroscopic functional calculated from the stego image and the same functional obtained from a decompressed, cropped, and recompressed stego image. The functionals are built from marginal and joint statistics of DCT coefficients. Because the features are calculated directly from DCT coefficients, conclusions can be drawn about the impact of embedding modifications on detectability. Three different steganographic paradigms are tested and compared. Experimental results reveal new facts about current steganographic methods for JPEGs and new design principles for more secure JPEG steganography. 1
Keyphrases
jpeg image future design feature-based steganalysis stego image steganographic scheme dct coefficient specific macroscopic new feature-based steganalytic method current steganographic method linear classifier feature vector new design principle previous blind approach detection method jpeg steganographic algorithm secure jpeg steganography different steganographic paradigm embedding mechanism joint statistic new fact experimental result
