  Linguistic steganography provides convenient implementation to hide messages,
particularly with the emergence of AI generation technology. The potential
abuse of this technology raises security concerns within societies, calling for
powerful linguistic steganalysis to detect carrier containing steganographic
messages. Existing methods are limited to finding distribution differences
between steganographic texts and normal texts from the aspect of symbolic
statistics. However, the distribution differences of both kinds of texts are
hard to build precisely, which heavily hurts the detection ability of the
existing methods in realistic scenarios. To seek a feasible way to construct
practical steganalysis in real world, this paper propose to employ human-like
text processing abilities of large language models (LLMs) to realize the
difference from the aspect of human perception, addition to traditional
statistic aspect. Specifically, we systematically investigate the performance
of LLMs in this task by modeling it as a generative paradigm, instead of
traditional classification paradigm. Extensive experiment results reveal that
generative LLMs exhibit significant advantages in linguistic steganalysis and
demonstrate performance trends distinct from traditional approaches. Results
also reveal that LLMs outperform existing baselines by a wide margin, and the
domain-agnostic ability of LLMs makes it possible to train a generic
steganalysis model (Both codes and trained models are openly available in
https://github.com/ba0z1/Linguistic-Steganalysis-with-LLMs).
