Image-to-image steganography is hiding one image in another image. However, hiding two secret images into one carrier image is a challenge today. The application of image steganography based on deep learning in real-life is relatively rare. In this paper, a new Steganography Convolution Neural Network (SteganoCNN) model is proposed, which solves the problem of two images embedded in a carrier image and can effectively reconstruct two secret images. SteganoCNN has two modules, an encoding network, and a decoding network, whereas the decoding network includes two extraction networks. First, the entire network is trained end-to-end, the encoding network automatically embeds the secret image into the carrier image, and the decoding network is used to reconstruct two different secret images. The experimental results show that the proposed steganography scheme has a maximum image payload capacity of 47.92 bits per pixel, and at the same time, it can effectively avoid the detection of steganalysis tools while keeping the stego-image undistorted. Meanwhile, StegaoCNN has good generalization capabilities and can realize the steganography of different data types, such as remote sensing images and aerial images.

中文翻译：

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SteganoCNN：基于卷积神经网络的具有泛化能力的图像隐写术


图像到图像隐写术是将一个图像隐藏在另一图像中。然而，将两个秘密图像隐藏到一个载体图像中是当今的一个挑战。基于深度学习的图像隐写术在现实生活中的应用还比较少见。本文提出了一种新的隐写卷积神经网络（SteganoCNN）模型，解决了将两幅图像嵌入到一个载体图像中的问题，可以有效地重建两幅秘密图像。 SteganoCNN 有两个模块，一个编码网络和一个解码网络，而解码网络包括两个提取网络。首先，对整个网络进行端到端训练，编码网络自动将秘密图像嵌入到载体图像中，解码网络用于重建两个不同的秘密图像。实验结果表明，所提出的隐写方案的最大图像负载能力为每像素47.92位，同时可以有效避免隐写分析工具的检测，同时保持隐写图像不失真。同时，StegaoCNN具有良好的泛化能力，可以实现遥感图像、航空图像等不同数据类型的隐写。