Linguistic Steganalysis via Probabilistic Weighted Contrastive Learning

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.