GVIS: Generative Vector Image Steganography

Vector images have attracted increasing attention in the field of information hiding in recent years due to their scalability and structural properties. However, existing steganographic methods for vector images often introduce noticeable modifications to the files themselves, resulting in potential security risks and limited embedding capacity. Motivated by recent advances in diffusion models and image generative steganography, we propose GVIS, a novel \textbf{G}enerative \textbf{V}ector \textbf{I}mage \textbf{S}teganography framework. GVIS deterministically generates bitmap images using diffusion models, which are subsequently vectorized into scalable vector images. On the sender side, we design a lightweight overlap detection algorithm to identify Bézier curve control points suitable for data embedding, which enables the secret information to be encoded into the polar coordinate parameters of these control points. Then, the receiver can use the pre-shared conditional inputs to reconstruct the generation process and accurate message extraction by vector difference. Extensive theoretical analysis and experimental results demonstrate that GVIS achieves high-capacity, high-accuracy, secure, and training-free steganography. To the best of our knowledge, this is the first work to introduce generative model into the domain of vector image steganography.