This repository implements StegaStamp, a CVPR 2020 research project on steganography using deep learning. The encoder-decoder architecture encodes arbitrary data into images while maintaining perceptual similarity, and the decoder can recover the hidden data even after the image has been printed and photographed, which introduces various corruptions. The code includes both pretrained models for inference and training scripts to reproduce results or train custom encoders and decoders.