Elements of information theory
Elements of information theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Defending against statistical steganalysis
SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
Image steganalysis with binary similarity measures
EURASIP Journal on Applied Signal Processing
Statistically undetectable jpeg steganography: dead ends challenges, and opportunities
Proceedings of the 9th workshop on Multimedia & security
On the capacity of stegosystems
Proceedings of the 9th workshop on Multimedia & security
The ultimate steganalysis benchmark?
Proceedings of the 9th workshop on Multimedia & security
A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th international conference on Information hiding
On steganographic embedding efficiency
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
YASS: yet another steganographic scheme that resists blind steganalysis
IH'07 Proceedings of the 9th 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
Towards multi-class blind steganalyzer for JPEG images
IWDW'05 Proceedings of the 4th international conference on Digital Watermarking
IH'04 Proceedings of the 6th international conference on Information Hiding
Pre-processing for adding noise steganography
IH'05 Proceedings of the 7th international conference on Information Hiding
IH'05 Proceedings of the 7th international conference on Information Hiding
A feature selection methodology for steganalysis
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Steganalysis using higher-order image statistics
IEEE Transactions on Information Forensics and Security
Steganalysis for Markov cover data with applications to images
IEEE Transactions on Information Forensics and Security
An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels
IEEE Transactions on Information Theory
The square root law of steganographic capacity
Proceedings of the 10th ACM workshop on Multimedia and security
Estimating the Information Theoretic Optimal Stego Noise
IWDW '09 Proceedings of the 8th International Workshop on Digital Watermarking
Using high-dimensional image models to perform highly undetectable steganography
IH'10 Proceedings of the 12th international conference on Information hiding
Benchmarking for steganography by kernel fisher discriminant criterion
Inscrypt'11 Proceedings of the 7th international conference on Information Security and Cryptology
Entropy Quad-Trees for High Complexity Regions Detection
International Journal of Software Science and Computational Intelligence
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With the increasing number of new steganographic algorithms as well as methods for detecting them, the issue of comparing security of steganographic schemes in a fair manner is of the most importance. A fair benchmark for steganography should only be dependent on the model chosen to represent cover and stego objects. In particular, it should be independent of any specific steganalytic technique. We first discuss the implications of this requirement and then investigate the use of two quantities for benchmarking--the KL divergence between the empirical probability distribution of cover and stego images and the recently proposed two-sample statistics called Maximum Mean Discrepancy (MMD). While the KL divergence is preferable for benchmarking because it is the more fundamental quantity, we point out some practical difficulties of computing it from data obtained from a test database of images. The MMD is well understood theoretically and numerically stable even in high-dimensional spaces, which makes it an excellent candidate for benchmarking in steganography. We demonstrate the benchmark based on MMD on specific steganographic algorithms for the JPEG format.