LAUSR

Steganography has received massive attention from the information-hiding community due to its excellent security for covert communication systems. Existing work focuses on improving security on single-modal media while cross-modal media is less explored. However, cross-modal interaction has become a prevalent social manner on current social networks, which arises potential behavioral security issues of single-modal steganography. In this letter, we propose a novel text steganography to explore the practicability of cross-modal steganography. The proposed scheme is composed with image encoder, message encoder, language model, and message extractor networks, where the generated stego texts are semantically consistent with the input reference image. In addition, current generative text steganography schemes are vulnerable to text attack based on synonym substitution since these heuristic algorithms embed information by constructing a mapping between secret messages and candidate tokens. Thus, we design a text attack layer based on synonym substitution to further improve the robustness of generated stego text. Experiments illustrate the superior performance of the proposed cross-modal steganography scheme in terms of security and robustness.