With the rapid development of Natural Language Processing (NLP) technologies, text steganography methods have been significantly innovated recently, which poses a great threat to cybersecurity. In this paper, we propose a novel attentional LSTM-CNN model to tackle the text steganalysis problem. 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 steganography texts. In addition, we apply attention mechanism to recognize and attend to important clues within suspicious sentences. After merge feature clues from Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), we use a softmax layer to categorize the input text as cover or stego. Experiments showed that our model can achieve the state-of-art result in the text steganalysis task.
翻译:随着自然语言处理(NLP)技术的迅速发展,最近对文本扫描方法进行了重大创新,这对网络安全构成了巨大的威胁。在本文中,我们提出了一个新的关注LSTM-CNN模型,以解决文本分层分析问题。拟议方法首先将单词映射到语义空间,以便更好地利用文本中的语义特征,然后利用进化神经网络(CNNs)和长短期内存(LSTM)的经常性神经网络(LSTM)的组合,以捕捉语义学文本中的本地和长距离背景信息。此外,我们运用关注机制识别和处理可疑句子中的重要线索。在将进化神经网络(CNNS)和常规神经网络(RNNS)的特征线索合并后,我们用软式马克斯层将输入文本分类为封面或外壳。实验显示,我们的模型可以在文本分层分析任务中取得最新结果。