用最优传输解决图像隐写中的信息失衡问题，提升隐写与还原质量。
StegOT: Trade-offs in Steganography via Optimal Transport
- 基于自编码器与最优传输设计多通道变换模块，均衡覆盖图与秘密图信息分布。
- 实验显示隐写图像与恢复图像质量均提升，实现覆盖与秘密信息的平衡。
- 适合关注隐写安全性和图像质量的视觉隐私研究者。
图像隐藏常被称为隐写术，旨在将一幅同分辨率的秘密图像嵌入载体图像中。现有大多数隐写模型基于生成对抗网络（GAN）和变分自编码器（VAE），但普遍存在模式崩溃问题。模式崩溃会导致隐写图像中载体与秘密图像间的信息失衡，进而影响后续提取效果。为此，本文提出StegOT，一种基于自编码器并融合最优传输理论的隐写模型。设计了多通道最优传输（MCOT）模块，将具有多个峰值的特征分布转换为单峰分布，实现信息的合理权衡。实验表明，本方法不仅实现了载体与秘密图像之间的信息平衡，还提升了隐写图像与恢复图像的质量。代码将发布于https://github.com/Rss1124/StegOT。
原文摘要 · Abstract (English)
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.
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