Random generation of combinatorial structures from a uniform
Theoretical Computer Science
Learning decision trees from random examples needed for learning
Information and Computation
Learning DNF under the uniform distribution in quasi-polynomial time
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Learning decision trees using the Fourier spectrum
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
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
Learning k&mgr; decision trees on the uniform distribution
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
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
A Markov process based approach to effective attacking JPEG steganography
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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In steganography secret messages are encoded into unsuspicious covertexts such that an adversary cannot distinguish the resulting stegotexts from original covertexts. To accomplish their respective tasks, encoder and adversary need information about the covertext distribution. In previous investigations, the knowledge about the covertext channel was highly unbalanced: while the adversary was granted full knowledge, the encoder could only query a black-box sampling oracle. In such a situation, the only general steganographic technique known is rejection sampling. But this method requires exponential sampling complexity with respect to the number of message bits per covertext document. The other extreme, a white-box setting, where the encoder knows the covertext distribution perfectly, resp. the distribution is efficiently computable, is also unrealistic in practice. To resolve these deficiencies and to get a finer-grained security analysis, we propose a new model, called grey-box steganography. Here, the encoder starts with at least some partial knowledge about the type of covertext channel. Using the sampling oracle, he first uses machine learning techniques to learn the covertext distribution and then tries to actively construct a suitable stegotext-either by modifying a covertext or by creating a new one. We illustrate our concept with three examples of concept classes of different complexity: channels that can be described by monomials, by decision trees and by DNF-formulae. Their learning complexity ranges from easily learnable up to (probably) difficult to learn. A generic construction is given showing that besides the learning complexity, the efficiency of grey-box steganography depends on the complexity of the membership test, and suitable modification procedures. For the concept classes considered we present efficient algorithms for changing a covertext into a stegotext.