With the widespread application of large language models (LLMs) in the field of natural language processing (NLP), copyright protection issues are becoming increasingly important.As an effective means of copyright protection, watermarking technology can help developers and users prove the copyright ownership of the model.However, existing watermarking methods struggle to optimize both watermark effectiveness and model performance simultaneously.To overcome this challenge, in this paper, we propose a backdoor watermarking method named CRMark based on Chain-of-Thought (CoT) and reinforcement learning.This method embeds backdoor symbols into the prompt of the datasets and adds copyright information as the watermark into the model response.Reinforcement learning is further employed to alleviate the performance degradation of the watermark model in normal tasks.Experimental results show that the CRMark does not reduce the model's original task performance while effectively maintaining the effectiveness of backdoor watermarks, with the watermark success rate of up to 96.5%.

With the rapid development of Natural Language Processing (NLP), more and more linguistic steganography methods have appeared in recent years, which may bring great challenges to the protection of cyberspace security. Due to the powerful feature extraction capabilities of Deep neural networks (DNN) to learn semantic features of large volumes of text, traditional steganalysis methods using manual features have gradually evolved into DNN-based methods. However, whether these DNN-based steganalysis methods can extract enough carrier features to achieve efficient steganalysis so that they can completely replace traditional methods based on handcrafted features remains an open question. To explore the answer, in this paper, we propose a new steganalysis method to integrate semantic and statistical features. We use BERT to extract semantic features and TF-IDF with AutoEncoder to obtain statistical features of the input text. Finally, we design a fusion mechanism to combine these two features. The experimental results show that due to the addition of statistical features, the proposed model can significantly improve the detection performance over current DNN-based linguistic steganalysis models.