Data hiding such as steganography and invisible watermarking has important applications in copyright protection, privacy-preserved communication and content provenance. Existing works often fall short in either preserving image quality, or robustness against perturbations or are too complex to train. We propose RoSteALS, a practical steganography technique leveraging frozen pretrained autoencoders to free the payload embedding from learning the distribution of cover images. RoSteALS has a light-weight secret encoder of just 300k parameters, is easy to train, has perfect secret recovery performance and comparable image quality on three benchmarks. Additionally, RoSteALS can be adapted for novel cover-less steganography applications in which the cover image can be sampled from noise or conditioned on text prompts via a denoising diffusion process. Our model and code are available at \url{https://github.com/TuBui/RoSteALS}.
@article{arxiv.2304.03400,
title = {RoSteALS: Robust Steganography using Autoencoder Latent Space},
author = {Tu Bui and Shruti Agarwal and Ning Yu and John Collomosse},
journal= {arXiv preprint arXiv:2304.03400},
year = {2023}
}