Did you mean: Text Steganalysis with Attentional LSTM-CNN.
The proposed method firstly maps words into semantic space for better exploitation of the semantic feature in texts and then utilizes a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) recurrent neural networks to capture both local and long-distance contextual information in ...
The proposed method firstly maps words into semantic space for bet- ter exploitation of the semantic feature in texts and then utilizes a combination of ...
Bao [33] proposed an attentional LSTM-CNN model to tackle the text steganalysis problem. The powerful supervised machine learning (ML) schemes remarkably ...
In this paper, we propose a novel attentional LSTM-CNN model to tackle the text steganalysis problem. ... In addition, we apply attention mechanism to recognize ...
The CNN based steganalysis model is able to capture some complex statistical de- pendencies and also learn feature representations.
Missing: Attentional | Show results with:Attentional
In this paper, we propose a meta-learning framework for text steganalysis in the few-shot scenario to ensure model fast-adaptation between tasks.
Experimental results show that the proposed text steganalysis method (TS-CNN) can achieve nearly 100\% precision and recall, outperforms all the previous ...
We propose a multi-task few-shot text steganalysis model based on Context-sensitive Prototypes, namely CP-Stega.
To fuse multi-granularity text features, we present a novel linguistic steganalysis method based on attentional bidirectional long-short-term-memory (BiLSTM) ...
This letter proposes a novel text steganalysis model based on convolutional neural network, which is able to capture complex dependencies and learn feature ...