Steganography is one of the important methods in the field of information hiding, which is the technique of hiding secret data within an ordinary file or message in order to avoid the detection of steganalysis models and human eyes. In recent years, many scholars have applied various deep learning networks to the field of steganalysis to improve the accuracy of detection. The rapid improvement of the accuracy of steganalysis models has caused a huge threat to the security of steganography. In addition, another important factor that limits the security of steganography is capacity. The larger the capacity, the worse and more unnatural the visual quality of carrier images after embedded. Therefore, this paper proposes a steganography model—HIGAN, which constructs the encoding network composed of residual blocks to hide the color secret image into another color image of the same size to output a lower distortion and higher visual quality steganographic image. Moreover, it utilizes the adversarial training between the encoder-decoder network and the steganalysis model to improve the ability to resist the detection of steganalysis models based on deep learning. The experimental results show that our proposed model is achievable and effective. Compared with the previous steganography model for hiding color images based on deep learning, the steganography model in this article could achieve steganographic images with higher visual quality and stronger security.

隐写术是信息隐藏领域中的重要方法之一，这是在普通文件或消息中隐藏秘密数据的技术，以避免检测隐写分析模型和人眼。近年来，许多学者将各种深度学习网络应用于隐写分析领域，以提高检测的准确性。隐写分析模型准确性的快速提高对隐写术的安全性造成了巨大威胁。另外，限制隐写技术安全性的另一个重要因素是容量。容量越大，嵌入后的载体图像的视觉质量越差且越不自然。因此，本文提出了一种隐写术模型——HIGAN，它构建了由残差块组成的编码网络，以将彩色秘密图像隐藏到另一个相同大小的彩色图像中，以输出较低的失真和较高的视觉质量隐写图像。此外，它利用编码器-解码器网络与隐写分析模型之间的对抗训练来提高抵御基于深度学习的隐写分析模型检测的能力。实验结果表明，我们提出的模型是可以实现和有效的。与以前的基于深度学习的隐藏彩色图像的隐写模型相比，本文中的隐写模型可以实现视觉质量更高，安全性更高的隐写图像。它利用编码器-解码器网络与隐写分析模型之间的对抗训练来提高抵抗基于深度学习的隐写分析模型的能力。实验结果表明，本文提出的模型是可行的和有效的。与以前的基于深度学习的隐藏彩色图像的隐写模型相比，本文中的隐写模型可以实现视觉质量更高，安全性更高的隐写图像。它利用编码器-解码器网络与隐写分析模型之间的对抗训练来提高抵抗基于深度学习的隐写分析模型的能力。实验结果表明，本文提出的模型是可行的和有效的。与以前的基于深度学习的隐藏彩色图像的隐写模型相比，本文中的隐写模型可以实现视觉质量更高，安全性更高的隐写图像。