Detection of LSB Steganography via Sample Pair Analysis
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
A Markov process based approach to effective attacking JPEG 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
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
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
Binary image steganographic techniques classification based on multi-class steganalysis
ISPEC'10 Proceedings of the 6th international conference on Information Security Practice and Experience
JPEG steganalysis using HBCL statistics and FR index
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
| Hi-index | 0.00 |
In this paper, we investigate our previously developed run-length based features for multi-class blind image steganalysis. We construct a Support Vector Machine classifier for multi-class recognition for both spatial and frequency domain based steganographic algorithms. We also study hierarchical and non-hierarchical multi-class schemes and compare their performance for steganalysis. Experimental results demonstrate that our approach is able to classify different stego images according to their embedding techniques based on appropriate supervised learning. It is also shown that the hierarchical scheme performs better in our experiments.