Steganography in Large Language Models

Summary

Researchers propose a steganography scheme for large language models built on the transformer architecture. The sender encodes secret data by using matrix multiplication to modify the coverage parameters of a specific transformer block during training, and a receiver with a secret key extracts it from the stego model. The authors report experimental results on embedding capacity, undetectability, and robustness.

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