Linguistic Steganalysis by Enhancing and Integrating Local and Global Features
Qiong Xu, Ru Zhang, Jianyi Liu
Abstract
With the improvement of steganography, the difference of statistical distribution caused by information hiding is getting smaller and smaller, increasing the difficulty of steganalysis. The existing steganalysis models value different features equally, while the differences in the importance of high-dimensional features are ignored. That is the influences of feature quality on model performance are not considered. To fill the gap, a novel linguistic steganalysis by enhancing and integrating local and global features is proposed in this letter. It extracts and integrates the features of two dimensions, namely local semantic features and global long-term dependencies, to construct a joint feature map. Then, to improve the quality of features, a group-wise enhancement mechanism is employed, which divides features into multiple groups, enhancing important features in each group while weakening the less important ones by generating an important coefficient for each sub-feature in each semantic group. Finally, the enhanced features are used to further extract high-quality text representation to help the classification module distinguish cover and stego texts more accurately. The experimental results show that the proposed model has better detection performance in multiple hidden scenarios. The ablation experiment manifests that using integrated and enhanced features to extract high-quality text representations can effectively improve the discrimination ability of the model.