RoSteALS: Robust Steganography using Autoencoder Latent Space
This work addresses practical steganography needs for copyright protection and privacy-preserved communication, offering a robust and easy-to-train solution.
The authors tackled the problem of robust steganography by proposing RoSteALS, which uses frozen pretrained autoencoders to embed payloads, achieving perfect secret recovery and comparable image quality on three benchmarks with a lightweight encoder of 300k parameters.
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}.