F-DDIM: A Featurized Denoising Diffusion Implicit Model for Facial Image Steganography

Abstract

Facial image steganography is crucial for privacy-preserving media transmission. Traditional embedding methods degrade image quality and are vulnerable to steganalysis, while GAN-based non-embedding approaches lack controllability and realism. Diffusion-based methods using textual prompts face two key issues: (1) security risks from interpretable prompts and (2) poor preservation of facial details. This paper presents Featurized Denoising Diffusion Implicit Models (F-DDIM), a novel non-embedding steganography framework. First, F-DDIM replaces explicit textual prompts with implicit image-based encoding, enhancing security. Second, it selectively refines facial regions for natural and high-quality recovery through iterative reconstruction. Third, it enables indistinguishable encryption without secret key sharing via a novel sub-code embedding algorithm. Fourth, a refinement step post-decoding improves the clarity and accuracy of recovered facial image details. Experimental results demonstrate that F-DDIM achieves superior image fidelity and robustness against transmission interference.