This paper introduces a novel, lightweight deep invertible steganography network (LiDiNet) for image-in-image steganography. Traditional methods, while hiding a secret image within a cover image, often suffer from contour shadows or color distortion, making the secret image easily detectable. Additionally, the superposition of multiple invertible networks may complicate network structures and introduce excessive parameters, making the network training and learning processes difficult. LiDiNet addresses these issues by employing multiple invertible neural networks (INNs) to create a pair of coupled invertible processes for image hiding and recovery. A key innovation is the invertible convolutional layer, which streamlines the affine coupling structure in each INN for improved information fusion. In addition, a series of adaptive coordination spatial-wise attention modules are integrated to enhance the network’s effectiveness in image hiding and recovery, thereby elevating the security of the steganography. LiDiNet’s lightweight structure ensures both high-capacity steganography and robustness against steganalysis. Extensive experiments across various image datasets demonstrate LiDiNet’s superior performance, particularly in visual quality and anti-steganalysis capability, compared to existing methods.

中文翻译：

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LiDiNet：用于图像中图像隐写术的轻量级深度可逆网络


本文介绍了一种用于图像中图像隐写术的新型轻量级深度可逆隐写网络（LiDiNet）。传统方法虽然将秘密图像隐藏在封面图像中，但经常会出现轮廓阴影或颜色失真，使得秘密图像很容易被检测到。此外，多个可逆网络的叠加可能会使网络结构变得复杂并引入过多的参数，使得网络训练和学习过程变得困难。 LiDiNet 通过采用多个可逆神经网络 (INN) 创建一对用于图像隐藏和恢复的耦合可逆过程来解决这些问题。一项关键创新是可逆卷积层，它简化了每个 INN 中的仿射耦合结构，以改进信息融合。此外，集成了一系列自适应协调空间注意模块，以增强网络在图像隐藏和恢复方面的有效性，从而提高隐写术的安全性。 LiDiNet 的轻量级结构确保了高容量隐写术和针对隐写分析的鲁棒性。与现有方法相比，跨各种图像数据集的大量实验证明了 LiDiNet 的卓越性能，特别是在视觉质量和反隐写分析能力方面。