TS-CNN：基于卷积神经网络的语义空间文本隐写分析
TS-CNN: Text Steganalysis from Semantic Space Based on Convolutional Neural Network
摘要：
隐写分析是网络安全领域的一个重要研究课题，它有助于识别公共网络中的隐蔽攻击。近两年来，随着自然语言处理技术的飞速发展，无遮盖隐写术得到了长足的发展。先前的文本隐写分析方法在这种新的隐写技术上显示出不令人满意的结果，并且仍然是一个未解决的挑战。与以往所有的文本隐写分析方法不同，本文提出了一种基于语义分析的文本隐写分析方法（TS-CNN），该方法利用卷积神经网络（CNN）提取文本的高层语义特征，并发现隐藏秘密信息前后在语义空间中的细微分布差异。为了训练和测试所提出的模型，我们收集并发布了一个大型文本隐写分析（CT-Steg）数据集，其中包含216，000个不同长度和不同嵌入率的文本。实验结果表明，该模型的查准率和查全率接近100%，优于以往的所有方法.此外，所提出的模型甚至可以估计内部隐藏信息的容量。这些结果有力地支持了利用秘密信息嵌入前后语义空间的细微变化进行文本隐写分析的可行性和有效性。
英文摘要：
Steganalysis has been an important research topic in cybersecurity that helps to identify covert attacks in public network. With the rapid development of natural language processing technology in the past two years, coverless steganography has been greatly developed. Previous text steganalysis methods have shown unsatisfactory results on this new steganography technique and remain an unsolved challenge. Different from all previous text steganalysis methods, in this paper, we propose a text steganalysis method(TS-CNN) based on semantic analysis, which uses convolutional neural network(CNN) to extract high-level semantic features of texts, and finds the subtle distribution differences in the semantic space before and after embedding the secret information. To train and test the proposed model, we collected and released a large text steganalysis(CT-Steg) dataset, which contains a total number of 216,000 texts with various lengths and various embedding rates. Experimental results show that the proposed model can achieve nearly 100\% precision and recall, outperforms all the previous methods. Furthermore, the proposed model can even estimate the capacity of the hidden information inside. These results strongly support that using the subtle changes in the semantic space before and after embedding the secret information to conduct text steganalysis is feasible and effective.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
