Name
Abstract | ||
|---|---|---|
In this paper, we introduce a new blind steganalysis method that can reliably detect modifications in audio signals due to steganography. Lossless data-compression ratios are computed from the testing signals and their reference versions and used as features for the classifier design. Additionally, we propose to extract additional features from different energy parts of each tested audio signal to retrieve more informative data and enhance the classifier capability. Support Vector Machine (SVM) is employed to discriminate between the cover- and the stego-audio signals. Experimental results show that our method performs very well and achieves very good detection rates of stego-audio signals produced by S-tools4, Steghide and Hide4PGP. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34129-8_1 | ICICS |
Keywords | Field | DocType |
new blind steganalysis method,support vector machine,classifier design,lossless data-compression ratio,lossless data-compression technique,audio steganalysis,audio signal,stego-audio signal,different energy part,classifier capability,additional feature | Steganography,Audio signal,Pattern recognition,Computer science,Support vector machine,Speech recognition,Artificial intelligence,Steganalysis,Data compression,Classifier (linguistics),Lossless compression | Conference |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fatiha Djebbar | 1 | 43 | 5.30 |
| Beghdad Ayad | 2 | 35 | 3.46 |