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.

Redundant steganalysis feature components in high-dimensional steganalysis feature of images increase the spatio-temporal complexity of steganalysis and even reduce the detection accuracy of the stego images. In order to reduce the image steganalysis feature dimension, improve the detection accuracy of the stego images and achieve fast feature selection, this paper proposes a general method for image steganalysis feature selection. Firstly, a feature metric algorithm based on the difference function is given, and this algorithm measures the difference of the steganalysis feature components between the cover image class and the stego image class, which provides the basis for selecting the steganalysis feature components contributing greatly to detect the stego images. Secondly, the Pearson correlation coefficient is improved and used to measure the correlation between the steganalysis feature components and the image classification results to provide the basis for removing the redundant steganalysis feature components. And then, by setting thresholds, the steganalysis feature components with a large difference function value are selected and with a small Pearson correlation coefficient are deleted. Finally, the steganalysis feature components retained are trained and detected as the final steganalysis feature. A series of experimental results indicate that this method can reduce the feature dimension effectively and the spatio-temporal complexity of steganalysis, while maintaining or even improving the detection accuracy of the stego images. Compared to existing steganalysis feature selection methods such as Fisher-GFR and Improved-Fisher, this method has a higher detection accuracy of the stego images after simplification.

To detect stego (steganographic text) in complex scenarios, linguistic steganalysis (LS) with various motivations has been proposed and achieved excellent performance. However, with the development of generative steganography, some stegos have strong concealment, especially after the emergence of LLMsbased steganography, the existing LS has low detection or cannot detect them. We designed a novel LS with two modes called LSG/C. In the generation mode, we created an LS-task “description” and used the generation ability of LLM to explain whether texts to be detected are stegos. On this basis, we rethought the principle of LS and LLMs, and proposed the classification mode. In this mode, LS-G/C deleted the LS-task “description” and used the “causalLM” LLMs to extract steganographic features. The LS features can be extracted by only one pass of the model, and a linear layer with initialization weights is added to obtain the classification probability. Experiments on strongly concealed stegos show that LS-G/C significantly improves detection and reaches SOTA performance. Additionally, LS-G/C in classification mode greatly reduces training time while maintaining high performance.

Intelligent transportation systems (ITS) use advanced technologies such as artificial intelligence to significantly improve traffic flow management efficiency, and promote the intelligent development of the transportation industry. However, if the data in ITS is attacked, such as tampering or forgery, it will endanger public safety and cause social losses. Therefore, this paper proposes a watermarking that can verify the integrity of copyright in response to the needs of ITS, termed ITSmark. ITSmark focuses on functions such as extracting watermarks, verifying permission, and tracing tampered locations. The scheme uses the copyright information to build the multi-bit space and divides this space into multiple segments. These segments will be assigned to tokens. Thus, the next token is determined by its segment which contains the copyright. In this way, the obtained data contains the custom watermark. To ensure the authorization, key parameters are encrypted during copyright embedding to obtain cipher data. Only by possessing the correct cipher data and private key, can the user entirely extract the watermark. Experiments show that ITSmark surpasses baseline performances in data quality, extraction accuracy, and unforgeability. It also shows unique capabilities of permission verification and tampered location tracing, which ensures the security of extraction and the reliability of copyright verification. Furthermore, ITSmark can also customize the watermark embedding position and proportion according to user needs, making embedding more flexible.