As a countermeasure to linguistic steganography, linguistic steganalysis aims to distinguish between texts containing hidden secret messages (stego) and natural texts (cover). Current semi-supervised steganalysis approaches rely on a self-training mechanism. However, in linguistic steganalysis, pseudo-label errors propagate through iterative training, causing the student model to reinforce incorrect stego-distribution associations, thereby impairing its discriminative ability for subtle linguistic perturbations. To address this challenge, we propose SALT-LS, a self-training linguistic steganalysis framework that integrates a closed feedback loop between student and teacher models alongside a dual-constraint mechanism to improve pseudo-labels. Unlike a conventional semi-supervised steganalysis approach, we compute prototype penalties from both same-class (rather than intra-class) and cross-class perspectives, enabling more effective use of labeled data. Furthermore, we introduce an advanced-updating strategy for the student model, which is combined with the dual-constraint mechanism, forming a closed feedback loop that continuously refines the teacher model's pseudo-label generation for robust steganalysis performance. Extensive experiments on six datasets, including widely used steganographic strategies and corpora, demonstrate that SALT-LS outperforms state-of-the-art models. Our code is available.

Yinghan Zhou, Juan Wen, Wanli Peng, Xue Yiming, ZiWei Zhang, Wu Zhengxian. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Linguistic steganalysis is a technique to distinguish whether a text carrier contains secret information via statistical features. Current state-of-the-art methods are caught in two constraints. First, they cannot make accurate predictions on unlearned text distributions. In other words, the performance relies on the consistency of the training and testing distributions. Second, sufficient samples are required to fine-tune these models to reach their optimal states. In this article, we break through these obstacles by developing an effective steganalysis framework in a few-shot scenario. We first build the meta-datasets to simulate the real-world steganalysis environment that contains multi-distributional source and target domains with sparse target-domain samples. Then we propose a few-shot linguistic steganalysis framework combined with an adversarial meta-training mechanism to learn task-transferable features from source task sets to target tasks. Extensive experiments conducted on benchmark datasets show our model has a stable capability to learn transferable knowledge in detecting steganalysis tasks with extremely few-shot samples. We also validate the effectiveness of the model through multi-class steganalysis experiments to identify extra steganographic information involving embedding algorithms and capacities. Our proposed framework is effectively demonstrated to compensate for the drawback of state-of-the-art methods and tremendously improve the detection performance.

Text steganography involves discreetly concealing sensitive messages within natural text, while text steganalysis serves as its counterpart by aiming to detect suspicious text that may contain embedded secret information. Detecting steganographic text has become increasingly difficult because evolving steganographic algorithms produce ever-changing text distributions. Consequently, few-shot text steganalysis, which identifies steganographic text with scarce examples regardless of its distribution has become a research hotspot. The state-of-the-art few-shot text steganalysis relies on the inter-class variance between classes, i.e., they behave satisfactorily in detecting large-variance classes while being incompetent in distinguishing confusable samples from similar steganographic settings. In this paper, we propose an Adversary-Refinement Framework for Text Steganalysis, namely ARTS, which employs a task-invariant extractor and a task-relevant projector to implement an “attract and repel” process. Specifically, in the “attract” stage, we align task-invariant features through adversarial training to shorten the intra-class distance. Afterward, the refined prototypes are projected to a new space in the “repel” stage, and then a refined penalty item is applied to enlarge the inter-class distance. Extensive experiments conducted in six datasets with different inter-class variances demonstrate the superiority of the proposed model over the SOTA models.

Linguistic steganography is becoming increasingly secure with the resurgence of generative language models and steganographic algorithms. Incorporating advanced objectives to optimize the steganalysis model is a promising solution for hard-to-detect samples. Previous research proves the effectiveness of supervised contrastive loss in detecting various linguistic steganography methods. However, it has not taken full advantage of label information and hard negative samples in contrastive learning. In this letter, we propose a dual-network architecture PWCL-Stega, which gives more weight to the confusable negative pairs in contrastive learning, guiding the encoder to identify more intricate patterns between them. We further align the distributions from the same sample through different dropout masks by minimizing bidirectional KL divergence to improve the quality of representations. Experimental results demonstrate that our method achieves significant improvements of up to 13% and surpasses baseline methods on major benchmarks, even outperforming the state-of-the-art results.

In recent years, large language models (LLM) have progressed rapidly, leading to growing concerns about the proliferation of difficult-to-distinguish AI-generated content. This has given rise to a range of issues, including fake news, academic fraud, phishing emails, posing significant dangers across various domains. However, current machine-generated text (MGT) detection methods still face challenges, including the need to access model's output logits or losses, which makes it unable to adapt to black-box scenarios in the real world, and difficult to deploy models with large parameter sizes. Therefore, we propose a compression-based lightweight network for MGT detection that leverages the ability of lossless compression to effectively extract features between categories. With fewer parameters, our framework achieves state-of-the-art performance in MGT detection under black box conditions. Experiments demonstrate that our approach performs exceptionally well on both Chinese and English datasets. Specifically, our method achieves a fulltext detection accuracy of 99.5%, surpassing the previous SOTA method.

Text steganography is becoming increasingly secure by eliminating the distribution discrepancy between normal and stego text. On the other hand, the existing cross-entropy-based steganalysis models struggle to distinguish subtle distribution differences and lack robustness regarding confusable samples. To enhance steganalysis accuracy on hard-to-detect samples, this paper draws on contrastive learning to design a text steganalysis framework incorporating supervised contrastive loss into the training process. This framework improves feature representation by pushing apart embeddings from different classes while pulling closer embeddings from the same class. The experimental results show that our method makes remarkable improvement compared to the four baseline models. Additionally, as the embedding rate increases, our method's advantages become increasingly apparent, with maximum improvements of 13.98%, 12.47%, and 13.65% over the baseline methods across three common linguistic steganalysis datasets, Twitter, IMDB, and News, respectively. Our code is available at https://github.com/Katelin-glt/SCL-Stega https://github.com/katelin-glt/SCL-Stega.

Text steganalysis is a technique to distinguish between steganographic text and normal text via statistical features. Current state-of-the-art text steganalysis models have two limitations. First, they need sufficient amounts of labeled data for training. Second, they lack the generalization ability on different detection tasks. In this paper, we propose a meta-learning framework for text steganalysis in the few-shot scenario to ensure model fast-adaptation between tasks. A general feature extractor based on BERT is applied to extract universal features among tasks, and a meta-learner based on attentional Bi-LSTM is employed to learn task-specific representations. A classifier trained on the support set calculates the prediction loss on the query set with a few samples to update the meta-learner. Extensive experiments show that our model can adapt fast among different steganalysis tasks through extremely few-shot samples, significantly improving detection performance compared with the state-of-the-art steganalysis models and other meta-learning methods.