UP4LS: User Profile Constructed by Multiple Attributes for Enhancing
  Linguistic Steganalysis
Yihao Wang Ruiqi Song Ru Zhang Jianyi Liu
Linguistic steganalysis (LS) tasks aim to effectively detect stegos generated
by linguistic steganography. Existing LS methods overlook the distinctive user
characteristics, leading to weak performance in social networks. The limited
occurrence of stegos further complicates detection. In this paper, we propose
the UP4LS, a novel framework with the User Profile for enhancing LS
performance. Specifically, by delving into post content, we explore user
attributes like writing habits, psychological states, and focal areas, thereby
building the user profile for LS. For each attribute, we design the identified
feature extraction module. The extracted features are mapped to
high-dimensional user features via deep-learning networks from existing
methods. Then the language model is employed to extract content features. The
user and content features are integrated to optimize feature representation.
During the training phase, we prioritize the distribution of stegos.
Experiments demonstrate that UP4LS can significantly enhance the performance of
existing methods, and an overall accuracy improvement of nearly 25%. In
particular, the improvement is especially pronounced with fewer stego samples.
Additionally, UP4LS also sets the stage for studies on related tasks,
encouraging extensive applications on LS tasks.