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：隐藏秘密信息,利用深神经网络隐藏秘密信息,可以大大提高嵌入率,提高秘密共享的效率。现有工作主要是设计嵌入率或花哨功能较高的DNN,在本文中,我们希望回答一些根本性问题:如何增加和确定深藏率的嵌入率。为此,我们首先提出一个新的本地深藏(LDH)计划,通过将大型秘密图像隐藏到覆盖图像的本地小区域,大大提高嵌入率。我们的计划包括三个DNN:隐藏、定位和披露。我们利用隐藏网络将一个秘密图像转换成一个小的不可察觉的紧凑密密密码,该密码嵌入一个覆盖图像的本地随机区域。定位网络通过在Stego图像中识别秘密代码的位置来协助披露过程,而披露网络则从这些已查明的本地区域恢复所有全尺寸的秘密图像。我们的LDH实现一个极高的嵌入率,即$16\time24美元(bpp),并展示共同图像扭曲的超强性。我们还进行全面实验,以不同质量的算法方法来评估我们不同质量的图像修复计划。