StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography
DOI:
https://doi.org/10.1609/aaai.v38i3.28051Keywords:
CV: Applications, CV: Low Level & Physics-based VisionAbstract
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.Downloads
Published
2024-03-24
How to Cite
Ke, X. ., Wu, H., & Guo, W. (2024). StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography. Proceedings of the AAAI Conference on Artificial Intelligence, 38(3), 2723-2731. https://doi.org/10.1609/aaai.v38i3.28051
Issue
Section
AAAI Technical Track on Computer Vision II