Asymptotically Optimum Perfect Universal Steganography of Finite Memoryless Sources

A solution to the problem of asymptotically optimum perfect universal steganography of finite memoryless sources with a passive warden is provided, which is then extended to contemplate a distortion constraint. The solution rests on the fact that Slepian’s Variant I permutation coding implements first-order perfect universal steganography of finite host signals with optimum embedding rate. The duality between perfect universal steganography with asymptotically optimum embedding rate and lossless universal source coding with asymptotically optimum compression rate is evinced in practice by showing that permutation coding can be implemented by means of adaptive arithmetic coding. Next, a distortion constraint between the host signal and the information-carrying signal is considered. Such a constraint is essential whenever real-world host signals with memory (e.g., images, audio, or video) are decorrelated to conform to the memoryless assumption. The constrained version of the problem requires trading off embedding rate and distortion. Partitioned permutation coding is shown to be a practical way to implement this trade-off, performing close to an unattainable upper bound on the rate-distortion function of the problem.

[1]  Phil Sallee,et al.  Model-Based Steganography , 2003, IWDW.

[2]  Christian Cachin,et al.  An information-theoretic model for steganography , 1998, Inf. Comput..

[3]  Toby Berger,et al.  Permutation codes for sources , 1972, IEEE Trans. Inf. Theory.

[4]  H. Robbins A Remark on Stirling’s Formula , 1955 .

[5]  Ian H. Witten,et al.  A comparison of enumerative and adaptive codes , 1984, IEEE Trans. Inf. Theory.

[6]  Ian H. Witten,et al.  Data Compression Using Adaptive Coding and Partial String Matching , 1984, IEEE Trans. Commun..

[7]  Sergio Verdu Teaching Lossless Data Compression , 2011 .

[8]  Daniil Ryabko,et al.  Asymptotically optimal perfect steganographic systems , 2009, Probl. Inf. Transm..

[9]  Niels Provos,et al.  Defending Against Statistical Steganalysis , 2001, USENIX Security Symposium.

[10]  Aaron D. Wyner,et al.  Coding Theorems for a Discrete Source With a Fidelity CriterionInstitute of Radio Engineers, International Convention Record, vol. 7, 1959. , 1993 .

[11]  Thomas Mittelholzer,et al.  An Information-Theoretic Approach to Steganography and Watermarking , 1999, Information Hiding.

[12]  Richard Clark Pasco,et al.  Source coding algorithms for fast data compression , 1976 .

[13]  Richard E. Newman,et al.  J3: High payload histogram neutral JPEG steganography , 2010, 2010 Eighth International Conference on Privacy, Security and Trust.

[14]  Jun Yu,et al.  Subset selection circumvents the square root law , 2010, Electronic Imaging.

[15]  D. H. Lehmer Teaching combinatorial tricks to a computer , 1960 .

[16]  Shlomo Shamai,et al.  Nested linear/Lattice codes for structured multiterminal binning , 2002, IEEE Trans. Inf. Theory.

[17]  Jorma Rissanen,et al.  Generalized Kraft Inequality and Arithmetic Coding , 1976, IBM J. Res. Dev..

[18]  Yuhong Yang Elements of Information Theory (2nd ed.). Thomas M. Cover and Joy A. Thomas , 2008 .

[19]  Gregory W. Wornell,et al.  The duality between information embedding and source coding with side information and some applications , 2003, IEEE Trans. Inf. Theory.

[20]  Pierre Raymond de Montmort Essay D'Analyse Sur Les Jeux De Hazard , 1980 .

[21]  Andreas Westfeld,et al.  F5-A Steganographic Algorithm , 2001, Information Hiding.

[22]  K. P. Subbalakshmi,et al.  Zero Kullback-Liebler Divergence Image Data Hiding , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[23]  Pedro Comesaña Alfaro,et al.  On the capacity of stegosystems , 2007, MM&Sec.

[24]  D. Slepian Permutation Modulation , 1965, Encyclopedia of Wireless Networks.

[25]  Tomás Pevný,et al.  The square root law of steganographic capacity , 2008, MM&Sec '08.

[26]  Peter Harremoës,et al.  Inequalities between entropy and index of coincidence derived from information diagrams , 2001, IEEE Trans. Inf. Theory.

[27]  Pierre Moulin,et al.  Data-Hiding Codes , 2005, Proceedings of the IEEE.

[28]  Max H. M. Costa,et al.  Writing on dirty paper , 1983, IEEE Trans. Inf. Theory.

[29]  Gérard D. Cohen,et al.  A nonconstructive upper bound on covering radius , 1983, IEEE Trans. Inf. Theory.

[30]  Ross J. Anderson,et al.  On the limits of steganography , 1998, IEEE J. Sel. Areas Commun..

[31]  Toby Sharp,et al.  An Implementation of Key-Based Digital Signal Steganography , 2001, Information Hiding.

[32]  Ying Wang,et al.  Perfectly Secure Steganography: Capacity, Error Exponents, and Code Constructions , 2007, IEEE Transactions on Information Theory.

[33]  Boris Ryabko,et al.  Fast enumeration of combinatorial objects , 2006, ArXiv.

[34]  Phil Sallee,et al.  Model-Based Methods For Steganography And Steganalysis , 2005, Int. J. Image Graph..

[35]  Elke Franz Steganography Preserving Statistical Properties , 2002, Information Hiding.

[36]  Andrew Chi-Chih Yao,et al.  The complexity of nonuniform random number generation , 1976 .

[37]  C. Laisant Sur la numération factorielle, application aux permutations , .

[38]  J. Vitter,et al.  Practical Implementations of Arithmetic Coding , 1991 .

[39]  H. Daniels Processes generating permutation expansions , 1962 .

[40]  S. Even,et al.  Derangements and Laguerre polynomials , 1976, Mathematical Proceedings of the Cambridge Philosophical Society.

[41]  Kenneth Lange,et al.  Applied Probability , 2003 .

[42]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .