Robust steganography enables secret information to be transmitted stealthily and accurately in lossy channels such as social channels and wireless channels. With the development of deep learning, robust steganography can be constructed using the generative model of deep neural networks. Two new robust steganographic frameworks are proposed on the basis of generative models, and two algorithms are proposed on these two frameworks to verify the effectiveness of the proposed framework. Experiments show that the two frameworks proposed are more flexible than existing robust steganographic frameworks. To further verify the validity of the framework, when compared with existing robust steganography based on deep learning, the generative robust steganography algorithm is shown to have a higher secret information embedding capacity and higher steganography image quality.

中文翻译：

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基于生成对抗网络的鲁棒图像隐写术框架

强大的隐写技术使秘密信息能够在社交渠道和无线渠道等有损渠道中隐秘且准确地传输。随着深度学习的发展，可以使用深度神经网络的生成模型来构造鲁棒的隐写术。在生成模型的基础上，提出了两个新的健壮的隐写术框架，并在这两个框架上提出了两种算法，以验证所提出框架的有效性。实验表明，所提出的两个框架比现有的健壮的隐秘术框架更具灵活性。为了进一步验证该框架的有效性，与现有基于深度学习的健壮隐写术相比，