  Linguistic steganalysis (LS) tasks aim to detect whether a text contains
secret information. Existing LS methods focus on the deep-learning model design
and they achieve excellent results in ideal data. However, they overlook the
unique user characteristics, leading to weak performance in social networks.
And a few stegos here that further complicate detection. We propose the UP4LS,
a framework with the User Profile for enhancing LS in realistic scenarios.
Three kinds of user attributes like writing habits are explored to build the
profile. For each attribute, the specific feature extraction module is
designed. The extracted features are mapped to high-dimensional user features
via the deep-learning model of the method to be improved. The content feature
is extracted by the language model. Then user and content features are
integrated. Existing methods can improve LS results by adding the UP4LS
framework without changing their deep-learning models. Experiments show that
UP4LS can significantly enhance the performance of LS-task baselines in
realistic scenarios, with the overall Acc increased by 25%, F1 increased by
51%, and SOTA results. The improvement is especially pronounced in fewer
stegos. Additionally, UP4LS also sets the stage for the related-task SOTA
methods to efficient LS.
