Steganography using Gibbs random fields

Many steganographic algorithms for empirical covers are designed to minimize embedding distortion. In this work, we provide a general framework and practical methods for embedding with an arbitrary distortion function that does not have to be additive over pixels and thus can consider interactions among embedding changes. The framework evolves naturally from a parallel made between steganography and statistical physics. The Gibbs sampler is the key tool for simulating the impact of optimal embedding and for constructing practical embedding algorithms. The proposed framework reduces the design of secure steganography in empirical covers to the problem of finding suitable local potentials for the distortion function that correlate with statistical detectability in practice. We work out the proposed methodology in detail for a specific choice of the distortion function and validate the approach through experiments.

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