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It is well known that the performance of current state-ofthe-
art steganalyzers for detection of LSB matching is highly
sensitive to the datasets from different sources and it is hard to
predict which types of image are suitable for a specific steganalyzer.
This paper proposes a good solution on the kind of decompressed
images. The proposed method makes use of the fact
that the noise residuals of DCT coefficients of decompressed images
are rather concentrated on zero and very sensitive to LSB
matching. Therefore, a 10-D feature vector is constructed by
using the higher-order absolute moments of noise residuals and
the FLD is introduced to classify cover and stego images. The
experimental results show that the scheme is almost perfect at
embedding rate 0.5 bpp and that it is the accuracy of 90.9% at
0.1 bpp superior to the AD-HCF and the WAM methods. Furthermore,
the accuracy of the proposed method is not affected
by different image sources. It means that it is reliable for any
decompressed images.
The proposed method has some limitations that we would like
to address in the future. First, it is only suitable for the kind of
decompressed grayscale images that are not processed by other
image operators during and after decompression. The future
work is to design an identifier that can detect the bitmap compression
history. When the bitmap is identified as the decompressed
image, the method begins to work. Another limitation
of the technique is that it works only as long as the exact JPEG
decompressor is known. Therefore, we will test how much detection
accuracy is lost under different decompression schemes
in the future work.
ACKNOWLEDGMENT
The authors would like to thank the anonymous reviewers for
their helpful comments on this paper and thank Dr. G. Doerr for
providing the source code of the WAM steganalyzer.