Image hiding is often referred to as steganography, which aims to hide a secret image in a cover image of the same resolution. Many steganography models are based on genera-tive adversarial networks (GANs) and variational autoencoders (VAEs). However, most existing models suffer from mode collapse. Mode collapse will lead to an information imbalance between the cover and secret images in the stego image and further affect the subsequent extraction. To address these challenges, this paper proposes StegOT, an autoencoder-based steganography model incorporating optimal transport theory. We designed the multiple channel optimal transport (MCOT) module to transform the feature distribution, which exhibits multiple peaks, into a single peak to achieve the trade-off of information. Experiments demonstrate that we not only achieve a trade-off between the cover and secret images but also enhance the quality of both the stego and recovery images. The source code will be released on https://github.com/Rss1124/StegOT.

翻译：图像隐藏通常被称为隐写术，其目标是将秘密图像隐藏于相同分辨率的载体图像中。现有隐写模型多基于生成对抗网络（GAN）和变分自编码器（VAE），但普遍存在模式坍塌问题。模式坍塌会导致隐写图像中载体与秘密图像的信息失衡，进而影响后续提取效果。为应对这些挑战，本文提出StegOT——一种融合最优传输理论的自编码器隐写模型。我们设计了多通道最优传输（MCOT）模块，将呈现多峰分布的特征转换为单峰分布，以实现信息权衡。实验表明，该方法不仅实现了载体图像与秘密图像间的权衡，同时提升了隐写图像与恢复图像的质量。源代码将发布于https://github.com/Rss1124/StegOT。