Name
Abstract | ||
|---|---|---|
With the extensive use of texts on social network, text steganography, which protects several sensitive messages by embedding secret data into normal texts, has attracted widespread attention. As an adversary, text steganalysis which reveals the existence of hidden messages is also important. Recently, Deep Neural Networks (DNNs) have led to significant improvements in text steganalysis. However, the deeper and wider DNNs cause the increase of inference time, which restricts the practicality of text steganalysis. In this paper, we propose an effective and real-time text steganalysis method based on multi-stage transfer learning to enhance inference efficiency and detection performance simultaneously. The experimental results show that the proposed text steganalysis method can outperform previously reported methods in terms of detection accuracy and inference efficiency. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LSP.2021.3097241 | IEEE SIGNAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Feature extraction, Training, Bit error rate, Transfer learning, Social networking (online), Knowledge engineering, Task analysis, Text steganalysis, transfer learning, knowledge distillation, pre-trained BERT | Journal | 28 |
ISSN | Citations | PageRank |
1070-9908 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wan-li Peng | 1 | 0 | 1.35 |
| Jinyu Zhang | 2 | 0 | 0.34 |
| Yiming Xue | 3 | 17 | 6.28 |
| Zhenghong Yang | 4 | 1 | 0.69 |