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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 7·5 min read
Text Steganography with Dynamic Codebook and Multimodal Large Language Model
TL;DR
A novel black-box text steganography method using a dynamic codebook and multimodal large language model enhances security and flexibility in secret message embedding. Experimental results indicate superior performance compared to traditional methods in embedding capacity and text quality.
✦ Why It Matters
Implement this dynamic codebook approach to enhance the security of your text-based communications today.
Key Takeaways
- Explore dynamic codebooks for improved text steganography security.
- Utilize multimodal large language models for better message embedding.
- Implement feedback optimization for accurate secret message extraction.
- Consider practical applications in online social networks for enhanced communication.
How It Works
The proposed method constructs a dynamic codebook that adapts based on shared session configurations, allowing for more secure and flexible message embedding. By leveraging a multimodal large language model, the system generates steganographic text that conceals secret messages effectively.
An encrypted mapping is used to embed these messages, while a feedback optimization mechanism based on reject sampling ensures that the extraction of the hidden messages is accurate and reliable.