Image steganography has emerged as a method of hiding secret data within an image file to ensure the security of the transmitted data. In this study, we propose an architecture named MDRSteg to unobtrusively hide a large-size image in another image based on a residual neural network with dilated convolution and multi-scale fusion. The architecture consists of an embedding network to hide the secret image in the cover-image and a revealing network to reveal the secret image from the stego-image, both networks are made up of fully convolutional residual modules. The networks are jointly trained with a loss function which is the combination of chi-square distance (CSD) and mean-square error. The proposed MDRSteg are trained and tested on three datasets, Labeled Faces in the Wild, Pascal visual object classes, and ImageNet. Extensive experiments have been done and the experimental results suggest that the proposed model can not only hide a large size image in another image with good invisibility and large hiding capacity (24 bits-per-pixel), but also exhibits good generalization ability. The experimental results also show that dilated convolution, multi-scale fusion, and combined CSD loss function have positive effects on the delicate image steganography results and proves that the model is practically useful for many applications.

中文翻译：

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MDRSteg：基于多尺度膨胀ResNet和组合卡方距离损失的大容量图像隐写术

图像隐写术已经成为一种在图像文件中隐藏机密数据以确保所传输数据的安全性的方法。在这项研究中，我们提出了一种名为MDRSteg的体系结构，该体系基于带有扩张卷积和多尺度融合的残差神经网络，将大尺寸图像毫不干扰地隐藏在另一幅图像中。该体系结构由一个用于将秘密图像隐藏在封面图像中的嵌入网络和一个用于从隐身图像中揭示秘密图像的显示网络组成，这两个网络均由完全卷积的残差模块组成。通过损失函数联合训练网络，该损失函数是卡方距离（CSD）和均方误差的组合。提议的MDRSteg在三个数据集上进行了训练和测试，这些数据集是“狂野中的带标签的面孔”，Pascal视觉对象类和ImageNet。已经进行了广泛的实验，实验结果表明，提出的模型不仅可以将大尺寸图像隐藏在具有良好的不可见性和较大的隐藏能力（每像素24位）的另一幅图像中，而且还具有良好的泛化能力。实验结果还表明，扩展的卷积，多尺度融合和组合的CSD损失函数对精细的图像隐写术结果具有积极影响，并证明该模型对许多应用实际上是有用的。