Official implementation of RoSteALS: Robust Steganography using Autoencoder Latent Space.
Update: our model is now live on HuggingFace.
We tested with pytorch 1.11, torchvision 0.12 and cuda 11.3, but other pytorch versions probably work as well. To reproduce the environment, please check dependencies.
Run the following script to download our 100-bit RoSteALS pretrained model checkpoint (520MB). It also downloads the frozen vq-f4 autoencoder necessary if you want to train your own model later:
bash download_models.sh
The checkpoint and the frozen autoencoder will be stored at models/RoSteALS/epoch=000017-step=000449999.ckpt and models/first_stage_models/vq-f4/model.ckpt respectively.
To embed a secret text to an image:
python inference.py --config models/VQ4_mir_inference.yaml --weight models/RoSteALS/epoch=000017-step=000449999.ckpt --secret Secrets --cover examples/monalisa.jpg --output examples/monalisa_secrets.png
We use BCH Error Code Correction which costs 40 bits of our 100-bit payload, thus the secret text should not exceed 7 characters (or 8 ASCII characters, as demonstrated in our live demo above).
To prepare data, you will need to provide path to the image directory and a list containing the relative paths of all training images. These paths are configured at models/VQ4_mir.yaml.
To replicate our model, you can download the MIRFlickR dataset from their official website. We only use 100k images so downloading the first tar file is enough. For completness, we enclose the train and validation lists at prep_data/mir_train2.csv and prep_data/mir_val2.csv respectively.
python train.py --config models/VQ4_mir.yaml --secret_len 100 --max_image_weight_ratio 10 --batch_size 4 -o saved_models
where batch_size=4 is enough to fit a 12GB GPU; adjust the max_image_weight_ratio parameter to control the trade-off between stego quality and secret recovery performance (lower value means lower stego quality but higher secret recovery, please check Fig.8 in our paper for more details).
Please note that all images will be resized to 256x256 during training (to match with the vq-f4 autoencoder), but our inference script supports embedding at an arbitrary resolution.
The code is inspired from Stable Diffusion and ControlNet.
@InProceedings{bui2023rosteals,
title={RoSteALS: Robust Steganography using Autoencoder Latent Space},
author={Bui, Tu and Agarwal, Shruti and Yu, Ning and Collomosse, John},
booktitle = {Proc. CVPR WMF},
year={2023}
}