StegFormer: Rebuilding the Glory of the Autoencoder-Based Steganography (AAAI-2024)

Xiao Ke, Huanqi Wu, Wenzhong Guo

The official pytorch implementation of the paper StegFormer: Rebuilding the Glory of the Autoencoder-Based Steganography.

[Project Page] [Paper] [Pretrain_model]

Abstract

Image hiding aims to conceal one or more secret images within a cover image of the same resolution. Due to strict capacity requirements, image hiding is commonly called large-capacity steganography. In this paper, we propose StegFormer, a novel autoencoder-based image-hiding model. StegFormer can conceal one or multiple secret images within a cover image of the same resolution while preserving the high visual quality of the stego image. In addition, to mitigate the limitations of current steganographic models in real-world scenarios, we propose a normalizing training strategy and a restrict loss to improve the reliability of the steganographic models under realistic conditions. Furthermore, we propose an efficient steganographic capacity expansion method to increase the capacity of steganography and enhance the efficiency of secret communication. Through this approach, we can increase the relative payload of StegFormer to 96 bits per pixel without any training strategy modifications. Experiments demonstrate that our StegFormer outperforms existing state-of-the-art (SOTA) models. In the case of single-image steganography, there is an improvement of more than 3 dB and 5 dB in PSNR for secret/recovery image pairs and cover/stego image pairs.

News

• 2024.2.29: update README
• 2024.4.23: update pretrain model

How to train StegFormer

• Please download the training dataset: DIV2K
• Modify DIV2K_path, path and model_name in the file config.py
• Training the model by train_StegFormer_single_image.py or train_StegFormer_multiple_image

  Note that: please modify num_secret in config.py to define the number of secret images.

How to test

• Run test_save_single_image_hiding.py to test StegFormer using DIV2K valid dataset and save the images in folder image
• Run test_multiple_image_hiding.py to test StegFormer in multi-image hiding

  Note that: please modify num_secret in config.py to define the number of secret images.

• Run test_StegFormer.py to calculate PSNR, SSIM, MAE and RMSE

Contact

If you have any questions, please contact wuhuanqi135@gmail.com.

Citation

If you find this work helps you, please cite:

@inproceedings{ke2024stegformer,
  title={StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography},
  author={Ke, Xiao and Wu, Huanqi and Guo, Wenzhong},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={3},
  pages={2723--2731},
  year={2024}