Deep hiding, concealing secret information using Deep Neural Networks (DNNs), can significantly increase the embedding rate and improve the efficiency of secret sharing. Existing works mainly force on designing DNNs with higher embedding rates or fancy functionalities. In this paper, we want to answer some fundamental questions: how to increase and what determines the embedding rate of deep hiding. To this end, we first propose a novel Local Deep Hiding (LDH) scheme that significantly increases the embedding rate by hiding large secret images into small local regions of cover images. Our scheme consists of three DNNs: hiding, locating, and revealing. We use the hiding network to convert a secret image in a small imperceptible compact secret code that is embedded into a random local region of a cover image. The locating network assists the revealing process by identifying the position of secret codes in the stego image, while the revealing network recovers all full-size secret images from these identified local regions. Our LDH achieves an extremely high embedding rate, i.e., $16\times24$ bpp and exhibits superior robustness to common image distortions. We also conduct comprehensive experiments to evaluate our scheme under various system settings. We further quantitatively analyze the trade-off between the embedding rate and image quality with different image restoration algorithms.

翻译：深度隐藏利用深度神经网络（DNNs）隐密地隐藏秘密信息，能显著提高嵌入率并提升秘密共享的效率。现有工作主要致力于设计具有更高嵌入率或新奇功能的DNNs。本文旨在回答一些基本问题：如何提高深度隐藏的嵌入率以及其决定性因素是什么。为此，我们首先提出一种新颖的局部深度隐藏（LDH）方案，该方案通过将大型秘密图像隐藏到载体图像的小局部区域中，显著提高了嵌入率。我们的方案由三个DNNs组成：隐藏网络、定位网络和恢复网络。我们使用隐藏网络将秘密图像转化为微不可见的紧凑秘密编码，并嵌入到载体图像的随机局部区域中。定位网络通过识别隐写图像中秘密编码的位置来辅助恢复过程，而恢复网络则从这些被识别的局部区域中恢复出所有全尺寸秘密图像。我们的LDH实现了极高的嵌入率（即$16\times24$ bpp），并对常见图像失真表现出卓越的鲁棒性。我们还进行了全面实验，在不同系统设置下评估了我们的方案。进一步地，我们通过不同图像恢复算法，定量分析了嵌入率与图像质量之间的权衡关系。