{"ID":2844975,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05319","arxiv_id":"2511.05319","title":"$\\mathbf{S^2LM}$: Towards Semantic Steganography via Large Language Models","abstract":"Despite remarkable progress in steganography, embedding semantically rich, sentence-level information into carriers remains a challenging problem. In this work, we present a novel concept of Semantic Steganography, which aims to hide semantically meaningful and structured content, such as sentences or paragraphs, in cover media. Based on this concept, we present Sentence-to-Image Steganography as an instance that enables the hiding of arbitrary sentence-level messages within a cover image. To accomplish this feat, we propose S^2LM: Semantic Steganographic Language Model, which leverages large language models (LLMs) to embed high-level textual information into images. Unlike traditional bit-level approaches, S^2LM redesigns the entire pipeline, involving the LLM throughout the process to enable the hiding and recovery of arbitrary sentences. Furthermore, we establish a benchmark named Invisible Text (IVT), comprising a diverse set of sentence-level texts as secret messages to evaluate semantic steganography methods. Experimental results demonstrate that S^2LM effectively enables direct sentence recovery beyond bit-level steganography. The source code and IVT dataset will be released soon.","short_abstract":"Despite remarkable progress in steganography, embedding semantically rich, sentence-level information into carriers remains a challenging problem. In this work, we present a novel concept of Semantic Steganography, which aims to hide semantically meaningful and structured content, such as sentences or paragraphs, in co...","url_abs":"https://arxiv.org/abs/2511.05319","url_pdf":"https://arxiv.org/pdf/2511.05319v2","authors":"[\"Huanqi Wu\",\"Huangbiao Xu\",\"Runfeng Xie\",\"Jiaxin Cai\",\"Kaixin Zhang\",\"Xiao Ke\"]","published":"2025-11-07T15:17:40Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CR\"]","methods":"[\"Large Language Model\",\"Language Model\",\"Generative Adversarial Network\"]","has_code":false}