$\mathbf{S^2LM}$: Towards Semantic Steganography via Large Language Models
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912805513330688 |
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| author | Wu, Huanqi Xu, Huangbiao Xie, Runfeng Cai, Jiaxin Zhang, Kaixin Ke, Xiao |
| author_facet | Wu, Huanqi Xu, Huangbiao Xie, Runfeng Cai, Jiaxin Zhang, Kaixin Ke, Xiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05319 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | $\mathbf{S^2LM}$: Towards Semantic Steganography via Large Language Models Wu, Huanqi Xu, Huangbiao Xie, Runfeng Cai, Jiaxin Zhang, Kaixin Ke, Xiao Computer Vision and Pattern Recognition Cryptography and Security 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. |
| title | $\mathbf{S^2LM}$: Towards Semantic Steganography via Large Language Models |
| topic | Computer Vision and Pattern Recognition Cryptography and Security |
| url | https://arxiv.org/abs/2511.05319 |