Name
Abstract | ||
|---|---|---|
The prevailing text steganalysis methods detect steganographic communication by extracting hand-crafted features and classifying them using SVM. However, these features are designed based on the statistical changes caused by steganography, thus they are difficult to adapt to different kinds of embedding algorithms and the detection performance is heavily dependent on the text size. In this letter,... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LSP.2019.2895286 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Feature extraction,Kernel,Convolution,Semantics,Convolutional neural networks,Adaptation models,Syntactics | Steganography,Embedding,Pattern recognition,Convolution,Convolutional neural network,Support vector machine,Artificial intelligence,Steganalysis,Word embedding,Sentence,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 3 | 1070-9908 |
Citations | PageRank | References |
3 | 0.38 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Wen | 1 | 11 | 3.17 |
| Xuejing Zhou | 2 | 4 | 0.73 |
| Ping Zhong | 3 | 10 | 3.16 |
| Yiming Xue | 4 | 17 | 6.28 |