mlsanityData to 14 Oct 2026

S2LM: Towards Semantic Steganography via Large Language Models

arXiv 2511.05319 ↗First posted 7 Nov 2025cs.CV cs.CRcited by 0

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Abstract (arXiv)

Despite remarkable progress in steganography, embedding sentence-level information into cover media remains challenging. In this work, we present the concept of Semantic-Payload Steganography, which aims to hide semantically meaningful and structured content, such as sentences, in cover media. Based on this concept, we present Sentence-to-Image Steganography as an instance that enables the hiding of sentence-level messages within a cover image. To accomplish this feat, we propose Semantic-Payload Steganographic Language Model (S2LM), which leverages large language models to embed high-level textual information into images. S2LM redesigns the entire pipeline, involving the LLM throughout the process to enable the hiding and recovery of sentences. To evaluate semantic-payload steganography methods, we establish a benchmark named Invisible Text (IVT), comprising a diverse set of corpus as secret messages. Experimental results demonstrate that S2LM effectively recovers natural-language messages while maintaining high visual quality. The source code will be released at https://github.com/aoli-gei/S2LM.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 37 of its 75 references that are in the map. First posted on arXiv 7 Nov 2025.

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References · 37

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25 more
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