Tree classifier design with a permutation statistic
Pattern Recognition
Instance-Based Learning Algorithms
Machine Learning
Averaging over decision stumps
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
IBM Systems Journal
Robust image watermarking in the spatial domain
Signal Processing
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
High capacity image steganography using wavelet-based fusion
ISCC '04 Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC"04) - Volume 02
Decision tree classifier for network intrusion detection with GA-based feature selection
Proceedings of the 43rd annual Southeast regional conference - Volume 2
Security, Steganography, And Watermarking Of Multimedia Contents VI (Proceedings of S P I E)
Security, Steganography, And Watermarking Of Multimedia Contents VI (Proceedings of S P I E)
Image steganalysis with binary similarity measures
EURASIP Journal on Applied Signal Processing
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Improved BSS Based Schemes for Active Steganalysis
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03
Blind Multi-Class Steganalysis System Using Wavelet Statistics
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 02
IH'04 Proceedings of the 6th 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
Optimized Feature Extraction for Learning-Based Image Steganalysis
IEEE Transactions on Information Forensics and Security
Multiclass Detector of Current Steganographic Methods for JPEG Format
IEEE Transactions on Information Forensics and Security
A feature-based classification technique for blind image steganalysis
IEEE Transactions on Multimedia
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
A virtual image cryptosystem based upon vector quantization
IEEE Transactions on Image Processing
Spread spectrum image steganography
IEEE Transactions on Image Processing
Steganalysis using image quality metrics
IEEE Transactions on Image Processing
| Hi-index | 12.05 |
The aim of this paper is to construct a practical forensic steganalysis tool for audio signals that can properly analyze the statistics disturbed by stego embedding and classify them to selected current steganographic methods. The objective of this paper is to prove that the choice of effective stego sensitive features and a proficient machine learning paradigm enhances the detection accuracy of the steganalyser. In this paper a rule based approach with a family of six decision tree classifiers viz., Alternating Decision Tree, Decision Stump, J48, Logical Model Tree, Naive Baye's Tree and Fast Decision Tree learner, to perform the detection of audio subliminal channel is introduced. In particular the higher order statistics extracted from the Hausdorff distance are investigated for an improvement of the detection performance, as competent audio steganalytic features. The evaluation of the enhanced feature space and the decision tree paradigm, on a database containing 4800 clean and stego audio files is performed for classical steganographic as well as for watermarking algorithms. With this strategy it is shown how general forensic approach can detect information hiding techniques in the field of covert communication as well as for DRM applications. For the latter case, the detection of the presence of a potential watermark in a specific feature space can lead to new attacks or to a better design of the watermarking pattern.