Glow Model-Based Latent Vector Optimization for Generative Image Steganography in Edge and Cloud Computing Environment.

In edge and cloud computing environments, to protect and manage secret information, the sharing and recovery of each secret image are implemented by local servers. However, since existing Generative Image Steganography(GIS) schemes face issues such as low-quality image generation and small hiding capacity. This necessitates the generation of a large number of stego-images to meet the demands of information transmission, thereby imposing a excessive computational burden for those local servers, the above reasons make the existing GIS schemes not suitable for edge and cloud computing environments. To address the above issue, we propose a Latent Vector Optimization(LVO) scheme for GIS with high-quality image generation and large hiding capacity. In the proposed scheme, we introduce the concept of latent vector optimization, wherein the hiding probability of each element within the latent vector is computed based on its expected influence on the quality of the resulting stego-image. Furthermore, our LVO scheme employs an adaptive approach to identify the optimal locations for embedding information while considering a predefined hiding capacity. This adaptation involves giving priority to modifying elements in dimensions characterized by a low latent vector hiding probability, as guided by the characteristics of natural images. Simultaneously, the scheme hides the secret message within elements associated with a high latent vector hiding probability, thus achieving a large hiding capacity while minimizing any adverse effects on the stego-image quality. Compared with the existing GIS schemes, the proposed LVO scheme enhances security, provides high-quality image generation, a large data hiding capacity, meeting the requirements with fewer stego-images. This significantly reduces the communication and computational burden on local servers.