  Generative linguistic steganography attempts to hide secret messages into
covertext. Previous studies have generally focused on the statistical
differences between the covertext and stegotext, however, ill-formed stegotext
can readily be identified by humans. In this paper, we propose a novel
zero-shot approach based on in-context learning for linguistic steganography to
achieve better perceptual and statistical imperceptibility. We also design
several new metrics and reproducible language evaluations to measure the
imperceptibility of the stegotext. Our experimental results indicate that our
method produces $1.926\times$ more innocent and intelligible stegotext than any
other method.
