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/