UP4LS: User Profile Constructed by Multiple Attributes for Enhancing Linguistic Steganalysis
This addresses the challenge of detecting hidden information in text for security applications, but it is incremental as it builds on existing deep-learning methods without changing their models.
The paper tackles the problem of linguistic steganalysis (LS) in social networks, where existing methods perform poorly due to overlooking user characteristics, and proposes UP4LS, a framework that enhances LS by incorporating user profiles, resulting in a 25% increase in accuracy and 51% increase in F1 score.
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.