为数字化表示文本内容的语义信息, 并提高基于同义词替换的隐写文本检测精度, 提出一种新的自然语言隐写分析方法.利用Word2vec对大规模语料库进行训练获得包含丰富语义信息的多维词向量, 使用同义词及其上下文词向量之间的余弦距离度量2个词之间的相关度, 并计算同义词在特定上下文中的合适度.根据信息嵌入过程中同义词替换操作对文本同义词合适度的影响提取检测特征形成特征向量, 采用贝叶斯分类模型训练特征向量得到隐写分析特征, 从而识别隐写文本.实验结果表明, 该方法对于不同嵌入率下隐写文本的平均检测精确率和召回率分别达到97.71％和92.64％, 具有较好的检测性能.%In order to represent the semantic information of the text content for digitization and improve the accuracy of detecting stego texts based on synonym substitution, a novel natural language steganalyisis method is proposed.Word2 vec is employed to train a large-scale corpus to obtain multi-dimensional word vectors which contains rich semantic information.Then, it uses the cosine distance between a synonym and its context word vector to measure the correlation between two words, and calculates the fitness of synonyms in a specific context.According to the effect on the context fitness of the synonyms caused by the synonym substitutions in the embedding process, detection features are extracted to form a feature vector, and the Bayesian classification model is employed to train feature vector for the task of steganalysis feature to detect the stego texts.Experimental results show that the proposed method has good detection performance, whose average detection precision and average recall for the stego texts with different embedding rates achieve 97.71％ and 92.64％, respectively.
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