Abstract: Abstract The rapid expansion of social networks has made data transmission over public channels ubiquitous, raising serious security and privacy concerns. Although encryption can guarantee confidentiality, its distinctive format may arouse suspicion. Steganography offers a covert alternative by hiding secret data within ordinary media—such as images, text, or audio—thereby reducing the risk of detection. Traditional pixel-editing steganographic methods are vulnerable to steganalysis or compression artifacts, while existing generative approaches often depend on tailored prompts or specific data sources, limiting their generalization and flexibility. To address these challenges, we propose a source-agnostic, target-domain image steganography framework based on a dual diffusion model. First, we employ a Latent Diffusion Model (LDM) to invert and optimize arbitrary secret images, obtaining high-fidelity latent representations. To eliminate residual structural cues from the LDM inversion that could compromise subsequent hiding and extraction, we introduce a Gaussian Structural Reordering Alignment module: it encrypts the secret’s latent representation via pseudo-random orthogonal perturbation and spatial shuffling, preserving its norm while enforcing a Gaussian-like distribution that aligns with downstream inputs and enhances security. Finally, we use a Denoising Diffusion Implicit Models (DDIM) sampler to denoise and decode the encrypted latent vectors, producing stego images that faithfully reflect the target domain’s appearance while carrying the hidden content. Experiments on three public datasets demonstrate that our method requires no source-specific training, achieves strong cross-domain generalization, and significantly outperforms competing techniques in extraction accuracy, imperceptibility, and resistance to steganalysis.