Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model
Honai Ueoka, Yugo Murawaki, Sadao Kurohashi · 2021
With advances in neural language models, the focus of linguistic steganography has shifted from edit-based approaches to generationbased 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 tradeoff.