Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Statistically undetectable jpeg steganography: dead ends challenges, and opportunities
Proceedings of the 9th workshop on Multimedia & security
The square root law of steganographic capacity
Proceedings of the 10th ACM workshop on Multimedia and security
On completeness of feature spaces in blind steganalysis
Proceedings of the 10th ACM workshop on Multimedia and security
International Journal of Computer Vision
Steganalysis by subtractive pixel adjacency matrix
Proceedings of the 11th ACM workshop on Multimedia and security
Less detectable JPEG steganography method based on heuristic optimization and BCH syndrome coding
Proceedings of the 11th ACM workshop on Multimedia and security
A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th international conference on Information hiding
Batch steganography and pooled steganalysis
IH'06 Proceedings of the 8th international conference on Information hiding
Modified matrix encoding technique for minimal distortion steganography
IH'06 Proceedings of the 8th international conference on Information hiding
MPSteg-color: a new steganographic technique for color images
IH'07 Proceedings of the 9th international conference on Information hiding
Gibbs construction in steganography
IEEE Transactions on Information Forensics and Security
Using high-dimensional image models to perform highly undetectable steganography
IH'10 Proceedings of the 12th international conference on Information hiding
A graph–theoretic approach to steganography
CMS'05 Proceedings of the 9th IFIP TC-6 TC-11 international conference on Communications and Multimedia Security
The Information Lost in Erasures
IEEE Transactions on Information Theory
Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes
IEEE Transactions on Information Forensics and Security - Part 2
An attempt to generalize distortion measure for JPEG steganography
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
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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.