  The rise of language models has provided a fertile ground for the application
of steganography. Due to their qualified output, steganographic texts become
similar to human and have attracted most of the steganography researchers'
attention. However, running a language model requires a strong computation
platform. It limits the applicable scenario of steganography, since those
electronic devices controlled by the decoder may not even equipped with a GPU.
Traditional provably secure steganography methods cannot be applied to this
low-resource scenario. Therefore, we aim at design a novel steganography
framework that is practical in a low-resource scheme. We start from the
rigorous probability analysis with the help of hypothesis testing techniques to
construct an theoretical framework. Then we prove the security and robostness
of our framework and point out its optimization goal. We test our theoretical
framework in some famous LLMs and the results have proved its usability. There
are still some practical problems and this gives the direction of future work.
We hope that this work will expand the practical scope of steganography and
create a new branch of steganography.
