Linguistic Steganalysis via Fusing Multi-Granularity Attentional Text Features  

作　　者：WEN Juan DENG Yaqian PENG Wanli XUE Yiming 

机构地区：[1]College of Information and Electrical Engineering,China Agricultural University,Beijing 100094,China

出　　处：《Chinese Journal of Electronics》2023年第1期76-84,共9页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (61872368, 61802410)。

摘　　要：Deep learning based language models have improved generation-based linguistic steganography,posing a huge challenge for linguistic steganalysis.The existing neural-network-based linguistic steganalysis methods are incompetent to deal with complicated text because they only extract single-granularity features such as global or local text features.To fuse multi-granularity text features,we present a novel linguistic steganalysis method based on attentional bidirectional long-shortterm-memory(BiLSTM) and short-cut dense convolutional neural network(CNN).The BiLSTM equipped with the scaled dot-product attention mechanism is used to capture the long dependency representations of the input sentence.The CNN with the short-cut and dense connection is exploited to extract sufficient local semantic features from the word embedding matrix.We connect two structures in parallel,concatenate the long dependency representations and the local semantic features,and classify the stego and cover texts.The results of comparative experiments demonstrate that the proposed method is superior to the state-of-the-art linguistic steganalysis.

关 键 词：Information hiding Natural language processing Linguistic steganalysis Attentional BiLSTM Dense connection 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]