A framework for synthetic stego
Article No.: 30, Pages 1 - 4
Abstract
We present a technique for hiding information in stochastic settings via data-synthesizing schemes based on transform-expand-sample (Tes) processes. The technique is applicable whenever data generated by an application or process is sufficiently complex to exhibit random but structured behavior (such as in collective data transforms), and data trajectories have viable alternatives that are unverifiable or simply hard to verify. In such cases, a synthesizing procedure generates novel data that either actually replaces, or is generated instead of, application or process data. When information can be hidden in such data at levels higher than typical levels of noise, message-neutralizing attacks will fail; and if synthetic data, stego data and application/process data cannot be distinguished, secure stego transmissions can be launched. An information-theoretic model shows that such hiding techniques are arbitrarily secure. We present some experimental results.
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- A framework for synthetic stego
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April 2009
952 pages
ISBN:9781605585185
DOI:10.1145/1558607
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Published: 13 April 2009
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CSIIRW '09
CSIIRW '09: Fifth Cyber Security and Information Intelligence Research Workshop
April 13 - 15, 2009
Tennessee, Oak Ridge, USA
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