SemVideoSte: Agentic AI for Cross-Modal Semantic Video Steganography Communication

Abstract

We propose an agentic artificial intelligence (AI)-driven cross-modal semantic video steganography communication (SemVideoSte) scheme for secure cross-modal information transmission. Agentic AI interprets the service context to determine a semantically matched public prompt and coordinates video generation, message embedding, semantic transmission, and message recovery process. A multi-mask mechanism combines variational autoencoder (VAE) reconstruction robustness, semantic transmission robustness, and latent separability to select reliable embedding positions. Meanwhile, a private digital token controls the random seed and guidance offset to construct positive and negative terminal latent states, enabling coverless message embedding during the final diffusion sampling step. Simulation results on open-source datasets demonstrate that SemVideoSte achieves high embedding capacity, reliable message recovery, and strong semantic consistency under different channel conditions. In addition, eavesdroppers without the correct digital token cannot reliably recover the secret message, and the generated stego videos effectively resist the steganalysis detection. We further establish a prototype testbed to verify the practical feasibility of SemVideoSte.