  With advances in neural language models, the focus of linguistic
steganography has shifted from edit-based approaches to generation-based ones.
While the latter's payload capacity is impressive, generating genuine-looking
texts remains challenging. In this paper, we revisit edit-based linguistic
steganography, with the idea that a masked language model offers an
off-the-shelf solution. The proposed method eliminates painstaking rule
construction and has a high payload capacity for an edit-based model. It is
also shown to be more secure against automatic detection than a
generation-based method while offering better control of the security/payload
capacity trade-off.
