2021/04/20 by Honai Ueoka, Ueoka, Honai, Yugo Murawaki +3 · 4 citations
Computer Science · #Advanced Steganography and Watermarking Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Natural Language Processing Techniques #cs.CL
paper · pdf · doi:10.48550/arxiv.2104.09833
7 pages, 4 firgures
arxiv created 2021/04/20 · openalex publication_date 2021/04/20 · arxiv updated 2021/04/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.