Kaushal Solanki, Ken Sullivan, Anindya Sarkar, U. Madhow, B.S.Manjunath, S. Chandrasekaran
Steganography is the art and science of communicating in such a way that the very existence of communication is not revealed to a third party. In order to communicate without being detected, the data-hider must obey following two conditions.
The objective in this project is to investigate steganographic schemes that can provide provable security by achieving zero Kullback-Leibler divergence between the cover and the stego signal distributions, while communicating at high rates.
We have used the principle of statistical restoration, where a certain fraction of the available coefficients are used for hiding while the rest is used to compensate for the changes in the host statistics due to hiding. By avoiding hiding in the low probability regions of the host distribution, we are able to achieve zero Kullback-Liebler divergence between the cover and stego distributions, even while embedding at high rates. The framework is applied to design practical schemes for image steganography, which are evaluated using supervised learning on a set of about 1000 natural images. For the presented JPEG steganography scheme, it is seen that the detector is indeed reduced to random guessing.
MATLAB code for JPEG-based steganography: zeroDivJPEGstego.m
Supporting files:Presented below are some sample results where we have applied the secure steganography algorithm on the baboon image. We have embedded 23300 bits in the 512 x 512 image, and we have used 19 AC DCT coefficients per 8 x 8 block for hiding. We use a hiding fraction of 30%; we hide in coefficients whose magnitude ≤ 30.
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| Original baboon image, before hiding |
Composite baboon image, with 23300 bits embedded in it |
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| Histogram of AC DCT coefficients available for hiding |
Histogram of AC DCT coefficients after hiding but before compensation |
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| Desired histogram for the compensation coefficients, to ensure zero KL divergence |
Final difference between the original and composite images, after compensation |
This research is supported by a grant from ONR #N00014-01-1-0380, ONR #N00014-05-1-0816.
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