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.

利用用户画像来提高语言隐写分析的性能，通过挖掘用户属性和内容特征，构建用户特征表示并使用深度学习网络，其中 UP4LS 方法在现有方法的基础上，通过考虑少量的隐写样本，显著提高了性能。

多属性构建的用户概况对于增强语言隐写分析的作用