Dual-stage encryption framework for secure 3D information hiding using single-pixel imaging and diffusion models

With the rapid advancement of three-dimensional (3D) imaging technologies, 3D data has been increasingly utilized in areas such as medical diagnostics, digital heritage preservation, and virtual reality, where protecting sensitive spatial information is essential. To address this need, 3D information hiding aims to securely embed depth data into a host image without introducing perceptible visual distortions. Unlike conventional two-dimensional (2D) information hiding, which focuses on pixel-level planar data, 3D hiding must preserve spatial structure, geometric consistency, and depth fidelity-posing greater challenges in terms of capacity, imperceptibility, and robustness. In this work, we propose, to our knowledge, a novel 3D information hiding method that integrates single-pixel imaging with diffusion models. A dual-stage encryption framework is employed to enhance both confidentiality and imperceptibility. Additionally, we introduce an adaptive weight allocation strategy that prioritizes regions of interest, thereby improving both hiding performance and recovery accuracy. Experimental results demonstrate that the proposed method significantly outperforms existing approaches in terms of hiding and recovery performance, showing strong potential for practical deployment in secure 3D data transmission and storage.