RLS-DTS: Reinforcement-Learning Linguistic Steganalysis in Distribution-Transformed Scenario

When the data undergo a distribution change, existing linguistic steganalysis often struggles to effectively capture the statistical characteristics of the transformed cover or stego, resulting in a drop in performances. To address this issue and fully use the information from the original data before the change, this letter proposes a reinforcement learning-based method for linguistic steganalysis. This method employs an agent (steganalyzer) to interact within an observation space, enabling adaptation to the characteristics of the transformed data and capturing steganalysis features. Specifically, we map the texts to the GloVe observation space and construct an agent comprising Actor module and Critic module to provide action, state, and other information. In the pre-training phase, agent trains and reinforces the Actor and Critic modules using the original data before the change. In the fine-tuning phase, agent optimizes these two modules to extract steganalysis feature through reinforcement training with instant reward in the transformed data. Experiments show that the proposed method exhibits better performances than the baseline in the transformed scenarios. Furthermore, this method offers a more autonomous training solution for linguistic steganalysis.