IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Detection of LSB Steganography via Sample Pair Analysis
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Steganography Preserving Statistical Properties
IH '02 Revised Papers from the 5th 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
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
A Markov process based approach to effective attacking JPEG steganography
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
Exploiting preserved statistics for steganalysis
IH'04 Proceedings of the 6th international conference on Information Hiding
IH'05 Proceedings of the 7th international conference on Information Hiding
A general framework for structural steganalysis of LSB replacement
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
Wet paper codes with improved embedding efficiency
IEEE Transactions on Information Forensics and Security
Steganalysis using higher-order image statistics
IEEE Transactions on Information Forensics and Security
Steganography using Gibbs random fields
Proceedings of the 12th ACM workshop on Multimedia and security
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
On dangers of overtraining steganography to incomplete cover model
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
"Break our steganographic system": the ins and outs of organizing BOSS
IH'11 Proceedings of the 13th 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
Moving steganography and steganalysis from the laboratory into the real world
Proceedings of the first ACM workshop on Information hiding and multimedia security
| Hi-index | 0.00 |
Blind steganalyzers can be used for many diverse applications in steganography that go well beyond a mere detection of stego content. A blind steganalyzer can also be used for constructing targeted attacks or as an oracle for designing steganographic methods. The feature space itself provides a low-dimensional model of covers useful for benchmarking. These applications require the feature space to be complete in the sense that the features fully characterize the space of covers. Incomplete feature sets may skew benchmarking scores and lead to poor steganalysis. As a simple test of completeness, we propose a general approach for constructing steganographic methods that approximately preserve the whole feature vector and thus become practically undetectable by any steganalyzer that uses the same feature set. We demonstrate the plausibility of this approach, which we call the Feature Correction Method (FCM) by constructing the FCM for a 274-dimensional feature set from a state-of-the-art blind steganalyzer for JPEG images.