FedKeyMIS: Keyed Federated Multi-image Steganography for Selective Extraction and Crosstalk Reduction

Diffusion-based image steganography can produce visually natural stego images, but most existing pipelines inject secret features uniformly across spatial locations and ignore that natural images do not tolerate perturbations equally across space. This becomes especially problematic for blind extraction: smooth regions reveal even weak perturbations, whereas textured regions can absorb stronger signals and remain decodable after distortion. We address this issue with AURA-Stega, an uncertainty-guided region-adaptive diffusion steganography framework for robust blind extraction. AURA-Stega estimates a latent-space uncertainty prior from the cover image and uses it to modulate secret-feature energy before deterministic diffusion inversion. As a result, the stego residual is concentrated in high-tolerance regions, while visually fragile regions are protected. We also consider asymmetric blind extraction, where the receiver recovers the message without access to the original cover image or the uncertainty map. Qualitative evidence shows that the uncertainty prior aligns with texture-rich regions and produces the expected residual pattern. To support this mechanism directly, we introduce a residual-concentration diagnostic in addition to standard recovery metrics.