Abstract
In this paper, we propose a Binary Coded Genetic Algorithm with Ensemble Classification feature selection procedure designed for steganalysis. Proposed feature selection method was used for searching the most appropriate subset of features from 22510 dimension feature space superior for JPEG steganal-ysis. Reduced set of features shows better classification accuracy for JPEG steganalysis compared to complete set of features. In our method we used an ensemble classifier to approximate the functional relationship between the reduced feature set and class label. Search for optimal subset of features requires to solve two optimization problems: define the optimal number of features and define the optimal subset itself. Proposed Binary Coded Genetic algorithm enables to solve two optimization problems together. Each feature is coded as a binary coefficient in a binary string, which represent one solution of the feature selection problem. Genetic operations executed for binary strings (parents) results new binary strings (child) with good chance to have higher classification accuracy for JPEG steganalysis. Experimental results clearly indicate the advantage of using the proposed reduced set of features for JPEG steganalysis.
| Original language | English |
|---|---|
| Title of host publication | IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings |
| Publisher | IEEE Computer Society |
| ISBN (Print) | 9781479928439 |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
| Event | 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 - Singapore, Singapore Duration: 2014 Apr 21 → 2014 Apr 24 |
Other
| Other | 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 14/4/21 → 14/4/24 |
Fingerprint
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
Cite this
Binary Coded Genetic Algorithm with Ensemble Classifier for feature selection in JPEG steganalysis. / Sachnev, Vasily; Kim, Hyong Joong.
IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings. IEEE Computer Society, 2014. 6827700.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Binary Coded Genetic Algorithm with Ensemble Classifier for feature selection in JPEG steganalysis
AU - Sachnev, Vasily
AU - Kim, Hyong Joong
PY - 2014/1/1
Y1 - 2014/1/1
N2 - In this paper, we propose a Binary Coded Genetic Algorithm with Ensemble Classification feature selection procedure designed for steganalysis. Proposed feature selection method was used for searching the most appropriate subset of features from 22510 dimension feature space superior for JPEG steganal-ysis. Reduced set of features shows better classification accuracy for JPEG steganalysis compared to complete set of features. In our method we used an ensemble classifier to approximate the functional relationship between the reduced feature set and class label. Search for optimal subset of features requires to solve two optimization problems: define the optimal number of features and define the optimal subset itself. Proposed Binary Coded Genetic algorithm enables to solve two optimization problems together. Each feature is coded as a binary coefficient in a binary string, which represent one solution of the feature selection problem. Genetic operations executed for binary strings (parents) results new binary strings (child) with good chance to have higher classification accuracy for JPEG steganalysis. Experimental results clearly indicate the advantage of using the proposed reduced set of features for JPEG steganalysis.
AB - In this paper, we propose a Binary Coded Genetic Algorithm with Ensemble Classification feature selection procedure designed for steganalysis. Proposed feature selection method was used for searching the most appropriate subset of features from 22510 dimension feature space superior for JPEG steganal-ysis. Reduced set of features shows better classification accuracy for JPEG steganalysis compared to complete set of features. In our method we used an ensemble classifier to approximate the functional relationship between the reduced feature set and class label. Search for optimal subset of features requires to solve two optimization problems: define the optimal number of features and define the optimal subset itself. Proposed Binary Coded Genetic algorithm enables to solve two optimization problems together. Each feature is coded as a binary coefficient in a binary string, which represent one solution of the feature selection problem. Genetic operations executed for binary strings (parents) results new binary strings (child) with good chance to have higher classification accuracy for JPEG steganalysis. Experimental results clearly indicate the advantage of using the proposed reduced set of features for JPEG steganalysis.
KW - Extreme learning machine
KW - JPEG steganography
KW - Steganalysis
KW - Undetectable data hiding
UR - http://www.scopus.com/inward/record.url?scp=84903725784&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84903725784&partnerID=8YFLogxK
U2 - 10.1109/ISSNIP.2014.6827700
DO - 10.1109/ISSNIP.2014.6827700
M3 - Conference contribution
AN - SCOPUS:84903725784
SN - 9781479928439
BT - IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
PB - IEEE Computer Society
ER -