Although steganography has made significant advancements in recent years, it still struggles to embed semantically rich, sentence-level information into carriers. However, in the era of AIGC, the capacity of steganography is more critical than ever. In this work, we present Sentence-to-Image Steganography, an instance of Semantic Steganography, a novel task that enables the hiding of arbitrary sentence-level messages within a cover image. Furthermore, we establish a benchmark named Invisible Text (IVT), comprising a diverse set of sentence-level texts as secret messages for evaluation. Finally, we present $\mathbf{S^2LM}$: Semantic Steganographic Language Model, which utilizes large language models (LLMs) to embed high-level textual information, such as sentences or even paragraphs, into images. Unlike traditional bit-level counterparts, $\mathrm{S^2LM}$ enables the integration of semantically rich content through a newly designed pipeline in which the LLM is involved throughout the entire process. Both quantitative and qualitative experiments demonstrate that our method effectively unlocks new semantic steganographic capabilities for LLMs. The source code will be released soon.

翻译：尽管隐写技术近年来取得了显著进展，但在将语义丰富的句子级信息嵌入载体方面仍面临挑战。然而，在AIGC时代，隐写容量比以往任何时候都更为关键。本工作提出了句子到图像的隐写方法——作为语义隐写的一个实例，这项新颖任务能够将任意句子级消息隐藏于载体图像中。此外，我们建立了一个名为Invisible Text（IVT）的基准数据集，其中包含多样化的句子级文本作为评估用秘密消息。最后，我们提出了$\mathbf{S^2LM}$：语义隐写语言模型，该模型利用大语言模型（LLMs）将高级文本信息（如句子甚至段落）嵌入图像。与传统比特级方法不同，$\mathrm{S^2LM}$通过新设计的流程实现了语义丰富内容的集成，其中LLM参与全过程。定量与定性实验均表明，我们的方法有效释放了LLMs在语义隐写方面的新能力。源代码即将发布。