# Semantic Steganography: A Framework for Robust and High-Capacity Information Hiding using Large Language Models

arXiv: https://arxiv.org/abs/2412.11043 · HTML: https://mlsanity.com/paper/2412.11043/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2024.

## Built on by (most cited first, 3 of 3)

- [Large AI Model-Enabled Secure Communications in Low-Altitude Wireless Networks: Concepts, Perspectives and Case Study](https://mlsanity.com/paper/2508.00256/) (2025)
- [S2LM: Towards Semantic Steganography via Large Language Models](https://mlsanity.com/paper/2511.05319/) (2025)
- [Synchronized Logit Steering: Real-world Steganography](https://mlsanity.com/paper/2608.14697/) (2026)

## Abstract (arXiv)

In the era of Large Language Models (LLMs), generative linguistic steganography has become a prevalent technique for hiding information within model-generated texts. However, traditional steganography methods struggle to effectively align steganographic texts with original model-generated texts due to the lower entropy of the predicted probability distribution of LLMs. This results in a decrease in embedding capacity and poses challenges for decoding stegos in real-world communication channels. To address these challenges, we propose a semantic steganography framework based on LLMs, which construct a semantic space and map secret messages onto this space using ontology-entity trees. This framework offers robustness and reliability for transmission in complex channels, as well as resistance to text rendering and word blocking. Additionally, the stegos generated by our framework are indistinguishable from the covers and achieve a higher embedding capacity compared to state-of-the-art steganography methods, while producing higher quality stegos.