Learning decision trees from random examples needed for learning
Information and Computation
Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
C4.5: programs for machine learning
C4.5: programs for machine learning
Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Machine Learning
CRYPTO '02 Proceedings of the 22nd Annual International Cryptology Conference on Advances in Cryptology
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Steganalysis of JPEG Images: Breaking the F5 Algorithm
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Learning from Positive and Unlabeled Examples
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
PAC Learning from Positive Statistical Queries
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
A Concrete Security Treatment of Symmetric Encryption
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
An information-theoretic model for steganography
Information and Computation
Upper and Lower Bounds on Black-Box Steganography
Journal of Cryptology
Bandwidth optimal steganography secure against adaptive chosen stegotext attacks
IH'06 Proceedings of the 8th international conference on Information hiding
On steganographic chosen covertext security
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Public-key steganography with active attacks
TCC'05 Proceedings of the Second international conference on Theory of Cryptography
Provably secure steganography with imperfect sampling
PKC'06 Proceedings of the 9th international conference on Theory and Practice of Public-Key Cryptography
Provably secure steganography and the complexity of sampling
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
On the limits of steganography
IEEE Journal on Selected Areas in Communications
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We propose a new model of steganography which combines partial knowledge about the type of covertext channel with machine learning techniques to learn the covertext distribution. Stegotexts are constructed by either modifying covertexts or creating new ones, based on the learned hypothesis. We illustrate our concept with channels that can be described by monomials. A generic construction is given showing that besides the learning complexity, the efficiency of secure grey-box steganography depends on the complexity of membership tests and suitable modification procedures. For the concept class monomials we present an efficient algorithm for changing a covertext into a stegotext.