Better Inversion of Diffusion Models for Generative Steganography

Traditional inversion algorithms attempt to directly invert the diffusion sampling equation. In this work, built on Latent Diffusion Models (LDMs), we propose a family of algorithms with varying time complexities that perform the search of an antecedent within the latent space and/or the Variational Autoencoder (VAE) decoder.