RIS-MoE: robust and secure image steganography via latent-space optimization with mixture-of-experts denoising

Abstract

Diffusion-based generative image steganography enables covert communication by synthesizing stego images without relying on cover images. However, existing latent-space methods still struggle to balance robustness, steganographic security, and visual fidelity, especially under practical channel distortions such as compression, blur, resizing, and noise. To address these challenges, we propose RIS-MoE, a robust and secure latent-space image steganography framework that integrates distortion-tolerant message representation with receiver-side adaptive latent restoration. At the sender side, a learnable orthogonal transformation converts the secret message into a distributed representation, which is embedded into the diffusion latent through a residual-guided Hide Network. At the receiver side, a plug-and-play Mixture-of-Experts (MoE) denoising module estimates the distortion composition and adaptively fuses specialized restoration experts before message extraction. Extensive experiments show that RIS-MoE achieves strong robustness under single, mixed, and real-world distortions. It maintains extraction accuracy above 90% under all evaluated simulated combined distortions and achieves 94.62% and 95.29% extraction accuracy after real-world Weibo and Instagram transmission, respectively. RIS-MoE also achieves competitive empirical resistance against spatial-domain, latent-domain, and diffusion-aware steganalyzers, while maintaining favorable visual quality with an FID of 7.35 and an LPIPS of 0.21 on Flickr8K. In addition, the proposed MoE module consistently improves representative latent-space steganography pipelines as a plug-and-play restoration component, demonstrating its transferability. The source code is publicly available at: https://github.com/angle-cell/RIS_MOE.