Image steganography is a technique of hiding secret information inside another image, so that the secret is not visible to human eyes and can be recovered when needed. Most of the existing image steganography methods have low hiding robustness when the container images affected by distortion. Such as Gaussian noise and lossy compression. This paper proposed PRIS to improve the robustness of image steganography, it based on invertible neural networks, and put two enhance modules before and after the extraction process with a 3-step training strategy. Moreover, rounding error is considered which is always ignored by existing methods, but actually it is unavoidable in practical. A gradient approximation function (GAF) is also proposed to overcome the undifferentiable issue of rounding distortion. Experimental results show that our PRIS outperforms the state-of-the-art robust image steganography method in both robustness and practicability. Codes are available at https://github.com/yanghangAI/PRIS, demonstration of our model in practical at http://yanghang.site/hide/.

翻译：图像隐写是一种将秘密信息隐藏于另一图像中的技术，使秘密信息对人眼不可见且可在需要时恢复。现有的大多数图像隐写方法在载体图像受高斯噪声、有损压缩等失真影响时，隐藏鲁棒性较低。本文提出PRIS以提升图像隐写的鲁棒性，该方法基于可逆神经网络，在提取过程前后部署两个增强模块，并采用三步训练策略。此外，针对现有方法常忽略但实际应用中不可避免的取整误差问题，本文提出梯度近似函数以克服取整失真的不可微难题。实验结果表明，PRIS在鲁棒性和实用性方面均优于当前最先进的鲁棒图像隐写方法。代码见https://github.com/yanghangAI/PRIS，实际应用演示见http://yanghang.site/hide/ 。