Text Steganography with Dynamic Codebook and Multimodal Large Language Model

TL;DR

This paper proposes a novel black-box text steganography method using a dynamic codebook and multimodal large language models to enhance security, flexibility, and embedding capacity.

Contribution

It introduces a dynamic codebook and feedback optimization mechanism, improving security, practicality, and performance over existing methods in text steganography.

Findings

Outperforms existing white-box methods in embedding capacity and text quality.

Achieves better practicality and flexibility than existing black-box methods.

Demonstrates effectiveness on online social networks.

Abstract

With the popularity of the large language models (LLMs), text steganography has achieved remarkable performance. However, existing methods still have some issues: (1) For the white-box paradigm, this steganography behavior is prone to exposure due to sharing the off-the-shelf language model between Alice and Bob.(2) For the black-box paradigm, these methods lack flexibility and practicality since Alice and Bob should share the fixed codebook while sharing a specific extracting prompt for each steganographic sentence. In order to improve the security and practicality, we introduce a black-box text steganography with a dynamic codebook and multimodal large language model. Specifically, we first construct a dynamic codebook via some shared session configuration and a multimodal large language model. Then an encrypted steganographic mapping is designed to embed secret messages during the steganographic caption generation. Furthermore, we introduce a feedback optimization mechanism based on reject sampling to ensure accurate extraction of secret messages. Experimental results show that the proposed method outperforms existing white-box text steganography methods in terms of embedding capacity and text quality. Meanwhile, the proposed method has achieved better practicality and flexibility than the existing black-box paradigm in some popular online social networks.